Free Essay

Face Recognition

In: Business and Management

Submitted By abolic
Words 7690
Pages 31
Intel® Technology Journal | Volume 18, Issue 4, 2014

HETERogEnEoUs FAcE REcognITIon: An EmERgIng
TopIc In BIomETRIcs
Contributor
Guodong Guo
West Virginia University

An emerging topic in biometrics is matching between heterogeneous image modalities, called heterogeneous face recognition (HFR). This emerging topic is motivated by the advances in sensor technology development that make it possible to acquire face images from diverse imaging sensors, such as the near infrared (NIR), thermal infrared (IR), and three-dimensional (3D) depth cameras. It is also motivated by the demand from real applications. For example, when a subject’s face can only be acquired at night, the NIR or IR imaging might be the only modality for acquiring a useful face image of the subject. Another example is that no imaging system was available to capture the face image of a suspect during a criminal act. In this case a forensic sketch, drawn by a police artist based on a verbal description provided by a witness or the victim, is likely to be the only available source of a face of the suspect.
Using the sketch to search a large database of mug-shot face photos is also a heterogeneous face recognition problem. Thus it is interesting to study the
HFR as a relatively new topic in biometrics. In this article, several specific HFR problems are presented, and various approaches are described to address the heterogeneous face matching problems. Some future research directions are discussed as well to advance the research on this emerging topic.

Introduction

“Biometrics is about the identification of humans by their characteristics or traits, which include both physiological and behavioral characteristics.” Biometrics is about the identification of humans by their characteristics or traits, which include both physiological and behavioral characteristics.
Physiological traits are related to the body shape, such as face, fingerprint, and iris, while behavioral characteristics are related to the pattern of human behavior, such as the typing rhythm, gait, and voice.
Because of the important and useful applications, such as identity management, law enforcement, and surveillance, biometrics has been an active research topic in the field of computer vision and pattern recognition.
Among various biometric traits, face recognition is one of the most challenging research topics, since there are many possible variations that affect the face matching performance. In traditional face recognition studies, the focus has been on addressing the changes and variations caused by human aging, head pose, illumination, and facial expressions, called A-PIE. Although significant progresses have been made especially for addressing the PIE problems, new challenges are emerging.
One of the emerging topics in face biometrics is matching between heterogeneous image modalities, called heterogeneous face recognition (HFR). This emerging

80 | Heterogeneous Face Recognition: An Emerging Topic in Biometrics

Intel® Technology Journal | Volume 18, Issue 4, 2014

topic is motivated by the advances in sensor technology development that make it possible to acquire face images from diverse imaging sensors, such as the near infrared (NIR), thermal infrared (IR), and three-dimensional (3D) depth cameras.
It is also motivated by the demand from real applications. For example, when a subject’s face can only be acquired at night, the NIR or IR imaging might be the only modality for acquiring a useful face image of the subject. Thus it is interesting to study the HFR as a relatively new topic in biometrics.
In this article, several specific problems belonging to HFR will be presented in the section “Heterogeneous Face Recognition Problems,” and different HFR algorithms and approaches will be introduced in the section “Heterogeneous
Face Recognition Algorithms.” Various HFR databases will be described briefly in the section “Heterogeneous Face Databases.” Future research directions for
HFR are discussed in the section “Some Thoughts on Future Directions. This is followed by “Concluding Remarks.”

Heterogeneous Face Recognition Problems
Dictionary.com defines heterogeneous as “diverse in kind or nature.” In the context of biometrics, heterogeneous face recognition (HFR) is to match face images coming from different modalities.[1] The motivation of the HFR is that face images of the same subject can often be captured by different sensors under different imaging conditions, because of the sensor technology development and broader application requirements.

“In the context of biometrics, heterogeneous face recognition (HFR) is to match face images coming from different modalities.”

For example, the sensors can use different spectral bands: visible light spectrum
(VIS), near infrared (NIR), and thermal infrared (IR); different content can be acquired: regular two-dimensional (2D) light reflection and three-dimensional
(3D) depth data, especially the recently developed RGB-D sensors. Further, the cameras can have different qualities with different prices, for example, high-quality professional cameras, low-quality surveillance or web cameras, or photo scanners; and can be used in different acquisition environments: indoor/ outdoor or different weather conditions (sunny, rainy, or snowy).
Therefore, in real applications, the probe and gallery face images may come from different image modalities. For instance, the still face images are usually used for face identity enrollment, while the face images from surveillance video cameras might be used for face matching or search over the still image database. “Therefore, in real applications, the probe and gallery face images may come from different image modalities.”

In this section, various HFR problems are discussed and presented, including both the basic problems that are clearly defined and have been studied in quite a few research works and some other HFR problems that have not been studied extensively.

Basic HFR Problems
The basic heterogeneous face matching problems include VIS vs. Sketch,
VIS vs. NIR, VIS vs. 3D, and VIS vs. IR. These specific problems have been

Heterogeneous Face Recognition: An Emerging Topic in Biometrics | 81

Intel® Technology Journal | Volume 18, Issue 4, 2014

clearly defined in previous research works[1][2], and are commonly admitted by researchers in biometrics.
Among the basic and typical heterogeneous face matching problems, VIS vs.
Sketch and VIS vs. NIR are the mostly studied in the literature.

“Compared to the popular VIS vs.
Sketch and VIS vs. NIR, there are far fewer publications on VIS vs. 3D and VIS vs. IR matching, although these problems are also defined clearly as heterogeneous face recognition problems.” There are also approaches performing heterogeneous matching between thermal
IR and VIS face images, for example, Li et al.[3], Choi et al.[4], Klare and
Jain[2], and approaches to perform recognition between forensic sketches and visible face images[5][2], which is much more challenging than viewed sketches
(drawn while viewing), because the drawn sketches can be obtained based on very limited information about the true identity, resulting in the sketches not being similar to the exact person. Compared to the popular VIS vs. Sketch and
VIS vs. NIR, there are far fewer publications on VIS vs. 3D and VIS vs. IR matching, although these problems are also defined clearly as heterogeneous face recognition problems.
There are several reasons why the problems of VIS vs. Sketch and VIS vs. NIR are more popular than others. One is that the high quality 3D range sensors and thermal IR cameras are still expensive, while the acquisition of NIR face images and face sketches does not need to involve expensive sensors. Thus it is relatively easier to collect data for research and practical applications, involving the Sketch, VIS, and NIR images. Another reason could be that it is more challenging to perform VIS vs. 3D or VIS vs. IR, since the image appearance differences between VIS and 3D or VIS and IR are significantly larger than between VIS and Sketch or VIS and NIR. As demonstrated by Goswami et al.[6], some photometric preprocessing of the images can help a lot to get high accuracies for heterogeneous face matching between VIS and NIR modalities.
The matching between VIS and Sketch can also have very high accuracies.[7]
The forensic sketches are more challenging than the viewed sketch[5][2]; that is because the forensic sketches drawn by the forensic artists may not know (or the witness may not remember) the “full” face correctly, and thus the limited information can result in the drawn sketches not characterizing the true person well. In other words, it does not really mean that the sketches and VIS are very different modalities.

3D

Sketch
VIS
NIR

IR

Figure 1: some typical pairwise, heterogeneous face matching problems
(source: West Virginia
University, 2014)

In addition to matching between VIS and other modalities, as shown in
Figure 1, there is also heterogeneous matching between any pair of modalities in practice, such as NIR vs. 3D or NIR vs. IR, when the diverse sensors are used more and more in practical applications. To keep the graphic illustration clean, those pairwise matching relations are not shown in Figure 1.
In early studies, researchers usually only dealt with one specific HFR problem, for example, VIS vs. Sketch, while in recent studies, multiple HFR problems were studied to validate the developed methods in different cases.
Not only the basic HFR problems but also some other newly proposed problems can be classified as heterogeneous face matching tasks, which will be introduced next.

82 | Heterogeneous Face Recognition: An Emerging Topic in Biometrics

Intel® Technology Journal | Volume 18, Issue 4, 2014

Other Heterogeneous Face Matching Problems
Some other face recognition problems in recent studies can be considered as heterogeneous face matching too. These atypical HFR problems include:
1. Matching between face images of different resolutions, that is, highresolution and low-resolution.[8][9] For this kind of study, some existing face databases were used to “generate” face images at different resolutions. For example, the face images are cropped[9] as 32×32 and then down-sampled to 16×16, 8×8, 6×6, and 4×4. These down-sampled low-resolution face images were up-sampled into 32×32 to mimic the low-resolution face images for their study.
2. Digital photo vs. video frame.[9] Face images can be captured by digital still cameras or extracted from the video sequences captured by video camcorders. The faces from digital photos and video frames can have different resolutions and qualities. Thus face matching between digital photos and video frames can also be considered as a heterogeneous face matching problem.[9]
3. Face recognition with cosmetic changes.[10][11] This can be considered as another heterogeneous face recognition problem. As shown in Figure 2, face images of the same subject may look very different based on whether

“Thus face matching between digital photos and video frames can also be considered as a heterogeneous face matching problem.”

Figure 2: Faces with makeup applied (left column) and faces with no makeup (right column) for the same individuals (each row).
(source: originally shown in Wen and guo[11], 2013)

Heterogeneous Face Recognition: An Emerging Topic in Biometrics | 83

Intel® Technology Journal | Volume 18, Issue 4, 2014

“The matching between face images with or without makeup can be considered as another heterogeneous face recognition problem.”

facial makeup is applied or not. The matching between face images with or without makeup can be considered as another heterogeneous face recognition problem.
Actually, it has been found that facial cosmetics can change the perception of faces significantly[12] and can bring a great challenge for face matching computationally.[13][14] Motivated by these studies, we have studied how to address the influence of makeup on face recognition based a dual-attributes approach[11], and a correlation-based approach.[10]

Heterogeneous Face Recognition Algorithms

“The key issue for heterogeneous face matching is how to reduce the difference between heterogeneous face images.” The key issue for heterogeneous face matching is how to reduce the difference between heterogeneous face images. Typically, there exist significant facial appearance changes between heterogeneous face images, even though the face images can be aligned well. The differences can be caused by the variety of sensors (for example, different spectral responses), different image acquisition conditions (for example, by physical devices or hand-drawing), or changes by the subjects themselves (for example, applying facial makeup). So the algorithm development for HFR usually focuses on various approaches to reduce the differences between heterogeneous face images of the same subjects.
Despite the significant progress that has been made for face recognition, most face recognition systems are not designed to handle HFR scenarios currently, including commercial off-the-shelf (COTS) systems. Therefore, there is a need and substantial interest for studying heterogeneous face matching problems.[2]
In this section, some representative approaches to HFR will be presented, based on a grouping into different categories.

Transforming One Modality to Another
To reduce the facial appearance differences between two modalities, one category of approaches is to transform the face images from modality A to another denoted by B, such that face matching can be executed using the
“same” modality B approximately. This transformation can be in the raw image level or feature level. If it is in the image level, a new image will be synthesized in modality B, and then the image comparison is likely to use the same modality B; If it is in the feature level, the extracted features from image modality A will be transformed into features in domain B, and then compared to the features extracted directly from image modality B. This kind of approach is typically used to deal with VIS and sketch matching, where a face sketch can be synthesized from a photograph (or vice versa).[15][16][7] There are also some other methods proposed purely for sketch synthesis[17][18], which may be useful for matching VIS and sketch images.
A representative method to sketch synthesis from face photos is the eigentransform method[15], which is similar to the eigenfaces method[19], but applied to two image modalities. The key idea is the sketch to be synthesized can be reconstructed based on the linear combination of a set of eigenvectors learned

84 | Heterogeneous Face Recognition: An Emerging Topic in Biometrics

Intel® Technology Journal | Volume 18, Issue 4, 2014

from training sketch images, and the combination coefficients are equal to those learned from the corresponding face photo reconstruction. Thus, given a face photo, the reconstruction coefficients can be learned first and then applied to the sketch synthesis from sketch eigenvectors. After synthesis, the pseudosketch can be used to match against real sketches in the gallery for recognition.
Other approaches[20][21][22] use the idea similar to image analogies[23] to transform one modality to another, such as NIR to VIS or vice versa. One representative method[20] is to use local patches to build a dictionary for
VIS and NIR faces separately and learn a linear combination of the nearest neighbors (similar patches) to reconstruct each patch for a given NIR face image. Then the learned linear reconstruction is applied to a new modality to synthesize a virtual VIS face for matching with other VIS images in the gallery.

Photometric Preprocessing
The second category of approaches to HFR is to use photometric preprocessing techniques to normalize the lighting or illumination in face images of each modality so that the differences between heterogeneous face images can be reduced. These preprocessing methods were originally developed to deal with illumination changes in visible light face images, but were then adapted to address the heterogeneous face matching problems, such as VIS vs. NIR face images. For these approaches, the underlying assumption is that the heterogeneity of face images is caused by the lighting or reflection differences in face surfaces.
Goswami et al.[6] gave a good summary of different photometric preprocessing techniques for HFR. Typically there are three different methods for photometric preprocessing, which will be introduced here:

“The second category of approaches to HFR is to use photometric preprocessing techniques to normalize the lighting or illumination in face images of each modality so that the differences between heterogeneous face images can be reduced.”

One method is called sequential chain (SQ) preprocessing. It uses a series of steps for face image preprocessing. First, the Gamma correction is executed, which enhances the local dynamic range of the face image in darker regions, while compressing the range in bright and highlight regions. Second, the
Difference of Gaussian (DoG) filtering is performed to compress the low frequency or nonessential information while maintaining or enhancing the gradient information that is more useful for recognition. Third, contrast equalization is used to rescale the intensity values globally and reduce the possibility of having extreme values during the processing in previous steps.
Another method is called single scale retinex (SSR). Usually the image intensity value, I, can be modeled as the product of illumination L and surface reflectance R. In the SSR method, the illumination component L is estimated by using the blurred image computed from the original face image. For example, the Gaussian filter can be used to compute the blurred image. Then the reflectance component R can be estimated by subtracting the illumination component from the original image in the logarithm domain. The SSR is applied to different modality images separately to compute the reflectance. The resulted reflectance images are assumed to be similar for heterogeneous face images, and are then used for feature extraction and matching.
Heterogeneous Face Recognition: An Emerging Topic in Biometrics | 85

Intel® Technology Journal | Volume 18, Issue 4, 2014

The third method is called self quotient image (SQI). The SQI is very similar to the SSR operation. It is defined by the ratio between the original face image and a smoothed version of the original image, without using the logarithm computation. The ratio image is then used for feature computation and matching, replacing the original face image.

“Currently the photometric preprocessing methods are mainly used for VIS vs. NIR face images.”

Currently the photometric preprocessing methods are mainly used for VIS vs.
NIR face images. As shown in Figure 3, various photometric preprocessing methods can make the NIR and VIS face images look more similar. However, it is not clear if these methods are useful or not for other heterogeneous face matching problems, such as VIS vs. IR or VIS vs. 3D.

Figure 3: The effect of photometric preprocessing on heterogeneous face images (top: VIs, bottom: nIR); left to right: raw images, sQ, sQI, and ssR processing results.
(source: originally shown in goswami et al.[6])

Another issue is that even though the photometric preprocessing can make the face images similar, it still needs feature mapping or other learning methods to further improve the performance for HFR in practice.

Common Subspace Projection
Modality
#1
Modality
#2

New
Features
Common Subspace
Projection

Figure 4: The common subspace projection to build the relationship between two different modalities of data and generate new features to minimize the differences
(source: West Virginia University, 2014)

The third category of HFR approaches is to generate common subspaces so that both modalities of face images can be projected into, and the differences between heterogeneous images are expected to be minimized after the projection, as illustrated in Figure 4. New features can be generated after the joint projections into the common space.
Classical methods to generate the common subspaces include the canonical correlation analysis (CCA)[24], and partial least squares (PLS).[25] These methods and their kernel versions for nonlinear mapping have been used for HFR, for example, by Sharma and Jacobs[8], Yang et al.[26], and
Yi et al.[27]

86 | Heterogeneous Face Recognition: An Emerging Topic in Biometrics

Intel® Technology Journal | Volume 18, Issue 4, 2014

Given face images corresponding to two different modalities, the CCA method can learn a pair of directions to maximize the correlation of the original data in the new subspace. The PLS is to learn a latent subspace such that the covariance between latent scores of the data from two modalities is maximized.
Both the CCA and PLS methods can have linear mapping and kernel based extensions for nonlinear mapping.
In addition to the classical methods, there are some other recent approaches to compute the common subspace in different ways. For example, Lin and Tang[28] proposed a method called Common Discriminant Feature
Extraction (CDFE) for inter-modality face recognition. Two transforms are simultaneously learned to transform the samples in both modalities respectively to the common feature space. The learning objective incorporates both the discriminative power and local smoothness of the feature transformation. “Both the CCA and PLS methods can have linear mapping and kernel based extensions for nonlinear mapping.”

Another method is the coupled discriminant analysis (CDA) by Lei et al.[9], which incorporates constraints such as locality information of the features and discriminative computation similar to the classical linear discriminant analysis (LDA), to improve the performance for heterogeneous face matching.
More recently, the kernel-prototype–based similarity measure for HFR[2] was proposed, which pursues the kernel trick by Balcan et al.[29] to represent each face image with a set of training images, serving as prototypes.

Random Subspaces
The random subspace (RS) method by Ho[30] was developed to deal with the small sample size problem in recognition, using the idea similar to the classical bagging[31] and random forests[32] methods. The RS method is also useful to improve and generalize the classification performance, based on sampling a subset of features and classifier training in the reduced feature space. Then multiple classifiers can be learned from the multiple sets of randomly sampled features. These classifiers can be combined together to form a much stronger classifier or recognizer.
Wang and Tang[33] used the random subspace with linear discriminant analysis
(LDA) called RS-LDA for visible light face recognition. Klare and Jain[34] adapted RS-LDA for heterogeneous face recognition, by using multiple samplings of face patches from both VIS and NIR face images. The random subspace is also extended to the kernel prototype similarity measures[2] for HFR.

“The random subspace (RS) method by Ho was developed to deal with the small sample size problem in recognition, using the idea similar to the classical bagging and random forests methods.”

Dual Attributes
Attributes are a semantic level description of visual traits, as discussed, for instance, by Lampert et al.[35] and Farhadi et al.[36] For example, a horse can be described as four legged, mammal, can run, can jump, and so on. A nice property of using attributes for object recognition is that the basic attributes might be learned from other objects, and shared among different categories of objects.[37]

Heterogeneous Face Recognition: An Emerging Topic in Biometrics | 87

Intel® Technology Journal | Volume 18, Issue 4, 2014

Facial attributes are a semantic level description of visual traits in faces, such as big eyes, or a pointed chin. Kumar et al.[38] showed that a robust face verification can be achieved using facial attributes, even if the face images are collected from uncontrolled environments over the Internet.

“The key idea is that the dual attributes can be learned from faces with and without cosmetics, separately.” Motivated by the usefulness of facial attributes, a method called dual attributes was recently proposed by Wen and Guo[11] for face verification robust to facial appearance changes caused by the makeup. The key idea is that the dual attributes can be learned from faces with and without cosmetics, separately.
Then the shared attributes can be used to measure facial similarity irrespective of cosmetic changes. In essence, dual attributes are capable of matching faces with or without makeup in a semantic level, rather than a direct matching with low-level features.
The dual attributes method by Wen and Guo[11] may be adapted to other heterogeneous face matching problems.

Multiview Discriminative Learning
In the methods introduced above, typically only two modalities are used for
HFR. Is it possible to deal with multiple modalities in the formulation? The answer is yes.
For example, the CCA can be extended to a multiview CCA by Rupnik and Shawe-Taylor.[39] Another way is to use the principle of LDA to derive a so-called multiview discriminant analysis (MDA) method by Kan et al.[40] It learns multiple view-specific linear transforms in a non-pairwise manner by optimizing a generalized Rayleigh quotient, that is, maximizing the betweenclass variations and minimizing within-class variations in a low dimensional subspace. The optimization problem is then solved by using the generalized eigenvalue decomposition technique.
Another method is the generalized multiview analysis by Sharma et al.[41], where the cross-view correlation is obtained from training examples corresponding to the same subjects or identities. This correspondence requirement is not needed in the MDA formulation.[40]

“These multiview analysis methods have been shown to be useful for some heterogeneous image matching

These multiview analysis methods[40][41] have been shown to be useful for some heterogeneous image matching problems, such as photo vs. sketch and
VIS vs. NIR.

problems, such as photo vs. sketch and

Heterogeneous Face Databases

VIS vs. NIR.”

To facilitate the study of heterogeneous face recognition, several databases have been assembled. A summary of the existing databases are presented in this section.

CUFS Database (Sketch-VIS)
This database was collected by the Chinese University of Hong Kong. The
CUHK Face Sketch Database contains 606 subjects with VIS and sketch face

88 | Heterogeneous Face Recognition: An Emerging Topic in Biometrics

Intel® Technology Journal | Volume 18, Issue 4, 2014

pairs.[7] There are 1,216 images in total. This is probably the first publicly available database for heterogeneous face matching.

CUFSF Database (Sketch-VIS)
This is an extended version of the CUFS database, containing 1,194 subjects with 2,388 image pairs of VIS and sketch by Zhang et al.[42] The sketch photos were drawn by artists when viewing the original face images for each subject. It is called viewed sketches by Klare and Jain[2] in contrast to the forensic sketches.

CASIA-HFB Database (VIS-NIR-3D)
This is probably the first database that contains more than two face modalities, assembled from the Institute of Automation, Chinese Academy of Sciences
(CASIA) by Li et al.[43] It has 100 subjects of 992 face images in total. Each subject has four VIS, four NIR, and one or two 3D face images. The cropped face images were provided with the eye coordinates aligned manually. Some baseline results were provided based on direct matching with the classical PCA and LDA features.
Later on, the database was extended to 202 subjects just for the VIS and NIR image modalities, resulting in 5,097 face images for VIS and NIR modalities.

“This is probably the first database that contains more than two face modalities, assembled from the
Institute of Automation, Chinese
Academy of Sciences…”

Cross-Spectral Dataset (VIS-NIR)
This dataset by Goswami et al.[6] contains VIS and NIR face pairs for 430 subjects over multiple sessions, collected from the University of Surrey in the United Kingdom. Different pose angles in pitch and yaw directions were captured for every 10 degrees. Each subject has at least three poses. In total, there are 2,103 NIR and 2,086 VIS face images. Twelve algorithms were provided as the baseline results together with the database, based on the combination of different photometric preprocessing methods, features, and matching techniques.

LDHF-DB (VIS-NIR, Long Distance)
This database by Maeng et al.[44] was collected by the Korea University. It contains 100 subjects at different distances to the cameras. Each subject was captured at distances of 60, 100, and 150 meters, separately, using both VIS and NIR cameras. There are 1,600 face images in total. This dataset emphasizes the long distance acquisition of heterogeneous face images.

UND Database (VIS-IR)
The database contains 82 subjects with multiple IR and VIS face images for each subject. The total number of face images in this database is 2,292. It was used by Choi et al.[4] for IR to VIS face recognition.

“This dataset emphasizes the long distance acquisition of heterogeneous face images.”

NPU3D Database (VIS-3D)
The NPU3D database by Zhang et al.[45] contains Chinese VIS and 3D faces, collected at Northwestern Polytechnical University, China, using the Konica
Minolta Vivid 910 3D laser scanner. The acquisition distance is about 1.5 meters.
There are 300 individuals captured with 35 different scans (various pose, facial expression, accessory and occlusion) per subject. In total, there are 10,500 3D facial surface scans with the corresponding VIS images.
Heterogeneous Face Recognition: An Emerging Topic in Biometrics | 89

Intel® Technology Journal | Volume 18, Issue 4, 2014

CASIA NIR-VIS 2.0 Database (VIS-NIR)
It contains 725 subjects of 17,580 face images from multiple recording sessions, in which the first session is identical to the CASIA-HFB database.
Each subject has 1–22 VIS and 5–50 NIR face images. Different evaluation protocols were also provided with the database as well by Li et al.[46]

Other Databases
There are also some other databases that are either small, seldom used, or just private, such as, for example, the VIS and IR face database collected by the Pinellas County Sheriff’s Office and forensic sketches and VIS databases, introduced by Klare and Jain[2].

Some Thoughts on Future Directions

“As an emerging topic in biometrics,
HFR has attracted more and more attention recently.”

As an emerging topic in biometrics, HFR has attracted more and more attention recently. However, the study of HFR is still in its early stage, and more efforts are needed to advance the field of research. Here some new thoughts are presented, hopefully to inspire new efforts to address the challenging research on HFR.

Identify Which Methods Can Work on Which HFR Problems
There are different modalities to match within HFR, such as Sketch vs. VIS, NIR vs. VIS, and so on. Different algorithms and approaches have been developed, which are typically for one specific HFR problem or two, but not for all. Even though an algorithm can be tested on different HFR problems experimentally, the recognition accuracies could be very different for different HFR problems.
For example, an algorithm can get 95-percent accuracy on VIS vs. sketch, but may only achieve 60-percent accuracy when applied to VIS vs. IR. So an issue is raised: which methods can work on which HFR problems? New investigations can be performed to address this issue, and then one can know which methods are appropriate to solve what kinds of HFR problems. It is especially important for real applications of biometrics systems, not just for academic research. A systematic evaluation of the existing (and future) algorithms on each of the HFR problems could be done towards addressing this issue.

Deal with the Degrees of Heterogeneity in HFR

“By defining and measuring the degrees of heterogeneity, one can know just how difficult it is to solve a specific HFR problem: the more heterogeneous, the more difficult to address typically.”

Related to the previous issue, another is to study and define the degrees of heterogeneity in various heterogeneous face matching problems. As presented earlier, there are a variety of HFR problems. However, it has not yet been studied just how heterogeneous it could be between two given modalities of face images. By defining and measuring the degrees of heterogeneity, one can know just how difficult it is to solve a specific HFR problem: the more heterogeneous, the more difficult to address typically.
Further, when a new HFR problem is proposed, one can predict how difficult it will be to address it before developing an algorithm to solve it, based on the measure of degrees of heterogeneity. The challenge is how to define and measure the degree of heterogeneity universally over different matching problems.

90 | Heterogeneous Face Recognition: An Emerging Topic in Biometrics

Intel® Technology Journal | Volume 18, Issue 4, 2014

And also, the measure of the heterogeneity can help classify the existing (and future) algorithms into different categories based on their capabilities to address the HFR problems at different levels of heterogeneity.

Explore New Learning Methods to Solve HFR Problems
As stated above, the study of HFR is still not mature; new algorithms are expected to be developed to improve the recognition performance. In developing new algorithms, one promising direction is to explore learningbased methods. Since it is difficult (if not impossible) to model how the image appearance is changed from one modality to another, example-based learning approaches are probably the only way to study the differences between two modalities and to build the relations between face images in two modalities.

“In developing new algorithms, one promising direction is to explore learning-based methods.”

In exploring learning-based methods, one direction is to study the domain adaptation methods to adapt the data from one modality to another.
Recently, we have shown that the adaptive support vector machines
(A-SVM) by Yang et al.[47] can be applied for action recognition from VIS to IR by Zhu and Guo[48]. Based on this, we can expect that the A-SVM or other domain adaptation methods could be helpful to address the
HFR problems.

Collect Larger Databases with Public Access
As stated earlier, some HFR databases have been assembled; however, few of them are large, compared to the homogeneous (same modality) face recognition databases. By collecting larger databases, one can evaluate the algorithm’s performance better towards real applications. Further, there are fewer databases for VIS vs. 3D, VIS vs. IR, makeup vs. no makeup, or containing multiple modalities for the same subjects. New databases can be collected to facilitate the study of various HFR problems, rather than just VIS vs. Sketch or VIS vs. NIR.

“By collecting larger databases, one can evaluate the algorithm’s performance better towards real applications.” Other HFR Problems
Some typical and atypical HFR problems were introduced earlier. However, new HFR problems can still be identified along with new sensor development or acquisition environment changes.
Further, some existing face recognition problems may be revisited by considering them as HFR. In this way, new ideas may be inspired to address the well-defined problems from new angles. For example, human aging can cause significant facial appearance changes, as shown in Figure 5. Cross-age face recognition is a well-defined, challenging problem. Various methods have been proposed, such as the generative approaches based on age synthesis by Gong et al.[49], Wu and Chellappa[50], Park et al.[51], Ramanathan and
Chellappa[52], and discriminative approaches by Yadav et al.[53], Li et al. [54], Ling et al.[55], and Biswas et al.[56] Because of the space limit, it will not be discussed in detail here, but the cross-age face recognition can be considered as a HFR problem as well.

Heterogeneous Face Recognition: An Emerging Topic in Biometrics | 91

Intel® Technology Journal | Volume 18, Issue 4, 2014

Figure 5: Aging can cause significant facial appearance changes.
(source: Image search over the Internet, 2014)

Concluding Remarks

“Hopefully this article will inspire new research efforts to address the challenging and interesting heterogeneous face recognition

An emerging topic in biometrics, called heterogeneous face recognition, has been presented. Several specific HFR problems, both typical and atypical, have been introduced. Some representative approaches to HFR have been described based on a categorization. Various HFR databases have been listed to researchers, and some new thoughts on future exploration of
HFR have been introduced as well. Hopefully this article will inspire new research efforts to address the challenging and interesting heterogeneous face recognition problems.

Complete References
[1]

Li, S., Encyclopedia of Biometrics (Springer: 2009).

[2]

Klare, B. and A. Jain, “Heterogeneous Face Recognition Using
Kernel Prototype Similarities,” IEEE Trans. on Pattern Analysis and
Machine Intelligence, vol. 35, No. 6, pp. 1410–1422, 2013.

[3]

Li, J., P. Hao, C. Zhang, and M. Dou, “Hallucinating Faces from
Thermal Infrared Images,” Proc. Int’l Conf. Image Processing, pp. 465–468, 2008.

[4]

Choi, J., S. Hu, S. Young, and L. Davis. “Thermal to Visible Face
Recognition.” Proc. of SPIE, Vol. 8371, pages 83711L–1, 2012.

[5]

Klare, B., Z. Li, and A. Jain, “Matching Forensic Sketches to
Mugshot Photos,” IEEE Trans. Pattern Analysis and Machine
Intelligence, vol. 33, no. 3, pp. 639–646, Mar. 2011.

[6]

problems.”

Goswami, D., C. Chan, D. Windridge, and J. Kittler, “Evaluation of face recognition system in heterogeneous environments (Visible vs NIR),” 2011 IEEE International Conference on Computer Vision
Workshops, pages 2160–2167.

92 | Heterogeneous Face Recognition: An Emerging Topic in Biometrics

Intel® Technology Journal | Volume 18, Issue 4, 2014

[7]

Wang, X. and X. Tang, “Face Photo-Sketch Synthesis and
Recognition,” IEEE Trans. Pattern Analysis and Machine
Intelligence, vol. 31, no. 11, pp. 1955–1967, Nov. 2009.

[8]

Sharma, A. and D. Jacobs. “Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch,” CVPR, pages 593–600, 2011.

[9]

Lei, Z., S. Liao, A. K. Jain, and S. Z. Li, “Coupled discriminant analysis for heterogeneous face recognition,” IEEE Trans. on
Information Forensics and Security, 7(6), 1707–1716, 2012.

[10]

Guo, G-D., L. Wen, and S. Yan, “Face authentication with makeup changes,” IEEE Trans. Circuits and Systems for Video
Technology, DOI:10.1109/TCSVT.2013.2280076

[11]

Wen, L. and G-D. Guo, “Dual attributes for face verification robust to facial cosmetics,” Journal of Computer Vision and Image
Processing, NWPJ-201301-82, Vol. 3, No. 1, pages 63–73, 2013.

[12]

Ueda, S. and T. Koyama, “Influence of make-up on facial recognition,” Perception, 39(2):260, 2010.

[13]

Dantcheva, A., C. Chen, and A. Ross, “Can facial cosmetics affect the matching accuracy of face recognition systems?” IEEE Conf. on Biometrics: Theory, Applications and Systems, Washington DC,
USA, 2012.

[14]

Chen, C., A. Dantcheva, and A. Ross, “Automatic facial makeup detection with application in face recognition,” Proc. of International Conference on Biometrics (ICB), Madrid, Spain,
June 2013.

[15]

Tang, X. and X. Wang, “Face Sketch Recognition,” IEEE Trans.
Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 50–57,
Jan. 2004.

[16]

Liu, Q., X. Tang, H. Jin, H. Lu, and S. Ma, “A Nonlinear
Approach for Face Sketch Synthesis and Recognition,” Proc.
IEEE Conf. Computer Vision and Pattern Recognition, pp. 1005–1010, 2005.

[17]

Gao, X., J. Zhong, J. Li, and C. Tian, “Face Sketch Synthesis
Algorithm Based on E-HMM and Selective Ensemble,” IEEE
Trans. Circuits and Systems for Video Technology, vol. 18, no. 4, pp. 487–496, Apr. 2008.

[18]

Zhang, W., X. Wang, and X. Tang, “Lighting and Pose Robust
Face Sketch Synthesis,” Proc. European Conf. Computer Vision,
2010.

Heterogeneous Face Recognition: An Emerging Topic in Biometrics | 93

Intel® Technology Journal | Volume 18, Issue 4, 2014

[19]

Turk, M. and A. Pentland, “Eigenfaces for recognition,”
J. Cognitive Neurosci., vol. 3, no. 1, pp. 71–86, 1991.

[20]

Chen, J., D. Yi, J. Yang, G. Zhao, S. Z. Li, and M. Pietikainen,
“Learning mappings for face synthesis from near infrared to visual light images,” IEEE Conf. on Computer Vision and Pattern
Recognition, pp. 156–163, 2009.

[21]

Wang, R., J. Yang, D. Yi, and S. Z. Li, “An analysis-by-synthesis method for heterogeneous face biometrics,” Advances in Biometrics
(Berlin: Springer 2009), pp. 319–326.

[22]

Liu, M., W. Xie, X. Chen, Y. Ma, Y. Guo, J. Meng, and Q. Qin,
“Heterogeneous face biometrics based on Gaussian weights and invariant features synthesis,” IEEE 2nd International Conference on
Computing, Control and Industrial Engineering (CCIE), Vol. 2, pp. 374–377, 2011.

[23]

Hertzmann, A., C. Jacobs, N. Oliver, B. Curless, and D. Salesin,
“Image analogies,” SIGGRAPH, 2001.

[24]

Hotelling, H., “Relations between two sets of variates.” Biometrika
28, 321–377 (1936).

[25]

Wold, H. “Partial least squares,” in Encyclopedia of Statistical
Sciences, edited by S. Kotz and N. Johnson, volume 6, pages 581–591. Wiley, New York, 1985.

[26]

Yang, W., D. Yi, Z. Lei, J. Sang, and S. Li, “2D-3D face matching using CCA,” IEEE International Conference on Automatic Face
Gesture Recognition, pages 1–6, 2008.

[27]

Yi, D., R. Liu, R. Chu, Z. Lei, and S. Z. Li, “Face matching between near infrared and visible light images,” IAPR International
Conf. on Biometric, pages 523–530, 2007.

[28]

Lin, D. and X. Tang, “Inter-Modality Face Recognition,” Proc.
European Conf. Computer Vision, pages 13–26, 2006.

[29]

Balcan, M.-F., A. Blum, and S. Vempala, “Kernels as Features: On
Kernels, Margins, and Low-Dimensional Mappings,” Machine
Learning, vol. 65, pp. 79–94, 2006.

[30]

Ho, T. K., “The Random Subspace Method for Constructing
Decision Forests,” IEEE Trans. Pattern Analysis and Machine
Intelligence, vol. 20, no. 8, pp. 832–844, 1998.

[31]

Breiman, L., “Bagging Predictors,” Machine Learning, vol. 24, pp. 123–140, 1996.

[32]

Breiman, L., “Random forests,” Machine Learning 45, No. 1
(2001): 5–32.

94 | Heterogeneous Face Recognition: An Emerging Topic in Biometrics

Intel® Technology Journal | Volume 18, Issue 4, 2014

[33]

Wang, X. and X. Tang, “Random Sampling for Subspace Face
Recognition,” Int’l J. Computer Vision, vol. 70, no. 1, pp. 91–104,
2006.

[34]

Klare, B. and A. Jain, “Heterogeneous face recognition: Matching
NIR to visible light images,” Int’l Conf. on Pattern Recognition,
2010, pp. 1513–1516.

[35]

Lampert, C., H. Nickisch, and S. Harmeling, “Learning to detect unseen object classes by between-class attribute transfer,” IEEE
Conf. on Computer Vision and Pattern Recognition, pages 951–958,
2009.

[36]

Farhadi, A., I. Endres, D. Hoiem, and D. Forsyth, “Describing objects by their attributes,” IEEE Conf. on Computer Vision and
Pattern Recognition, pages 1778–1785, 2009.

[37]

Farhadi, A., I. Endres, and D. Hoiem, “Attribute-centric recognition for cross-category generalization,” IEEE Conf. on
CVPR, pages 2352–2359, 2010.

[38]

Kumar, N., A. Berg, P. Belhumeur, and S. Nayar, “Attribute and simile classifiers for face verification,” IEEE International Conf. on
Computer Vision, pages 365–372, 2009.

[39]

Rupnik, J. and J. Shawe-Taylor, “Multi-view canonical correlation analysis,” SiKDD, 2010.

[40]

Kan, M., S. Shan, H. Zhang, S. Lao, and X. Chen, “Multi-view discriminant analysis,” European Conf. on Computer Vision, pp. 808–821 (2012).

[41]

Sharma, A., A. Kumar, H. Daume III, and D. W. Jacobs,
“Generalized multiview analysis: A discriminative latent space,” IEEE Conference on Computer Vision and Pattern
Recognition (2012).

[42]

Zhang, W., X. Wang, and X. Tang, “Coupled InformationTheoretic Encoding for Face Photo-Sketch Recognition,”
Proc. IEEE Conf. Computer Vision and Pattern Recognition,
2011.

[43]

Li, S. Z., Z. Lei, and M. Ao, “The HFB face database for heterogeneous face biometrics research.” IEEE Computer
Vision and Pattern Recognition Workshops, pp. 1–8, 2009.

[44]

Maeng, H., S. Liao, S.-W. Lee, and A. K. Jain. “Nighttime face recognition at long distance: cross-distance and crossspectral matching,” Asian Conf. on Computer Vision, pp. 708–721. 2012.

Heterogeneous Face Recognition: An Emerging Topic in Biometrics | 95

Intel® Technology Journal | Volume 18, Issue 4, 2014

[45]

Zhang, Y., Z. Guo, Z. Lin, H. Zhang, and C. Zhang, “The
NPU Multi-case Chinese 3D Face Database and Information
Processing,” Chinese Journal of Electronics, vol. 21, no. 2 (2012):
283–286.

[46]

Li, S. Z., D. Yi, Z. Lei, and S. Liao, “The CASIA NIR-VIS 2.0 face database,” Computer Vision and Pattern Recognition Workshops
(CVPRW), pp. 348–353, 2013.

[47]

Yang, J., R. Yan, and A. Hauptmann, “Cross-Domain Video
Concept Detection using Adaptive SVMs,” Proc. MM, 2007.

[48]

Zhu, Y. and G-D. Guo, “A study on visible to infrared action recognition,” IEEE Signal Processing Letters, Vol. 20, No. 9, pages 897–900, 2013.

[49]

Gong, D., Z. Li, D. Lin, J. Liu, and X. Tang, X. “Hidden Factor
Analysis for Age Invariant Face Recognition,” ICCV 2013.

[50]

Wu, T. and R. Chellappa, “Age invariant face verification with relative craniofacial growth model,” Computer Vision–ECCV 2012
(pp. 58–71).

[51]

Park, U., Y. Tong, and A. K. Jain, “Age-invariant face recognition,”
IEEE Transactions on Pattern Analysis and Machine Intelligence
32(5), 947–954.

[52]

Ramanathan, N. and R. Chellappa, “Face verification across age progression,” IEEE Trans. Image Process., vol. 15, no. 11, pp. 3349–3361, 2006.

[53]

Yadav, D., M. Vatsa, R. Singh, and M. Tistarelli, “Bacteria
Foraging Fusion for Face Recognition across Age Progression,”
Computer Vision and Pattern Recognition Workshops (CVPRW),
(2013) pp. 173–179.

[54]

Li, Z., U. Park, and A. K. Jain, “A discriminative model for age invariant face recognition,” IEEE Transactions on Information
Forensics and Security, (2011) 6(3), 1028–1037.

[55]

Ling, H., S. Soatto, N. Ramanathan, and D. W. Jacobs, “Face verification across age progression using discriminative methods,”
IEEE Transactions on Information Forensics and Security, (2010)
5(1), 82–91.

[56]

Biswas, S., G. Aggarwal, and R. Chellappa, “A non-generative approach for face recognition across aging,” in IEEE Second
Int’l Conf. on Biometrics: Theory, Application and Systems,
2008.

96 | Heterogeneous Face Recognition: An Emerging Topic in Biometrics

Intel® Technology Journal | Volume 18, Issue 4, 2014

Author Biography
Guodong Guo received his BE degree in Automation from Tsinghua
University, Beijing, China, in 1991, a PhD in Pattern Recognition and
Intelligent Control from the Chinese Academy of Sciences, in 1998, and a
PhD in computer science from the University of Wisconsin-Madison, in 2006.
He is currently an assistant professor in the Lane Department of Computer
Science and Electrical Engineering at West Virginia University. In the past, he has visited and worked in several places, including INRIA, Sophia Antipolis,
France, Ritsumeikan University, Japan, Microsoft Research, China, and North
Carolina Central University. He won the North Carolina State Award for
Excellence in Innovation in 2008, and Outstanding Researcher (2013–2014) and New Researcher of the Year (2010–2011) at CEMR, WVU. He was selected as the “People’s Hero of the Week” by BSJB under MMTC on July
29, 2013. His research areas include computer vision, machine learning, and multimedia. He is the author of Face, Expression, and Iris Recognition
Using Learning-based Approaches (2008), co-editor of Support Vector Machines
Applications (2014), and has published over 60 technical papers in face, iris, expression, and gender recognition, age estimation, and multimedia information retrieval. He can be contacted at Guodong.Guo@mail.wvu.edu

Heterogeneous Face Recognition: An Emerging Topic in Biometrics | 97

Copyright of Intel Technology Journal is the property of Intel Corporation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use.

Similar Documents

Free Essay

Face Recognition

...REVIEWS, REFINEMENTS AND NEW IDEAS IN FACE RECOGNITION Edited by Peter M. Corcoran Reviews, Refinements and New Ideas in Face Recognition Edited by Peter M. Corcoran Published by InTech Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2011 InTech All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by InTech, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Mirna Cvijic Technical Editor Teodora Smiljanic Cover Designer Jan Hyrat Image Copyright hfng, 2010. Used under license from Shutterstock.com First published July, 2011 Printed in Croatia A free online edition of this book is available...

Words: 33246 - Pages: 133

Premium Essay

Essay On Face Recognition

...Abstract— Face recognition is a biometric system used to verify or identify a person from a digital image. This involve extracting features of face and then recognize it, regardless of expression, lighting, illumination, transformations (translate, rotate and scale image), ageing and pose. The existing approaches are deblurring based, joint deblurring and recognition, deriving blur invariant features and direct approach, which uses convolution model for performing face recognition in the presence of blur. So these methods cannot handle non uniform blurring situations that frequently arise from rotations and tilts in hand held cameras. In this paper, face recognition is done, in the presence of space varying motion blur. We have taken the concept that the set of all images obtained by non-uniformly blurring a given image form a convex set. We develop an algorithm based on TSF (Transformation Spread Function) model. On each focused...

Words: 1130 - Pages: 5

Premium Essay

Face Recognition

...Re  Face Recognition Paper Adriana Zachry Psych/560 November 13, 2012 Christopher Wessinger Face Recognition Paper Face recognition develops slowly through life. Recognizing a face can be a difficult for the individual and also for the brain system that processes. The complexity of recognizing individual faces can be a difficult task at times. Recognizing faces also includes looking at an individual’s emotional expression and then, being able to take that information and processing it. This can be more complicated because facial recognition also includes the processing of emotions and emotional content. The brain can easily recognize a face without encountering any complications. Facial identification is essential for recognition of people in the social context within our society. The basic process of visual perception includes translating incoming stimulus into a perception and memory. When an individual will initially sees an object or a person, this information then gets processed through the brain. Bottom up and top down processing plays a critical role in object recognition. When we first look at an object we process it. This is called bottom up processing. When people apply previous knowledge to that object, it is known as top down processes. There is also a process when we recognize an object; we match an incoming object with stored information that helps us to recognize what is before us. A study was conducted by Palmer, Rsich and Chase on the perspective...

Words: 1117 - Pages: 5

Premium Essay

Face Recognition

...DECO HCI Seminar WS 2010/2011: Student projects stefan.bachl January 19, 2011 Tags: android, application, hci, ios, mobile, open data, prototyping, seminar This semester’s design task of the Human Computer Interaction seminar at DECO was to create, prototype and evaluate a location-aware mobile application that uses open data. Five teams of three students each completed the seminar, resulting in creative, useful and sometimes also provoking use of fictitious open data. As open datain Austria is in its infancy (for more information visit open3.at orgov.opendata.at), teams had difficulties finding existing open (government) data sources. Some teams solved this problem by augmenting their application ideas with user-generated content as the main data source. The horizontal prototypes for the evaluation were implemented with various tools, includingTitanium Mobile, Sencha Touch and also native development on iOS and Android platforms. We proudly present the mobile application concepts of our five participating student teams. Note that most screens contain static data used only for the evaluation of the prototypes. The applications are (also if stated otherwise) not available on any store at the moment. ------------------------------------------------- Team AAA_Team: RegioBioFood Aleksandar Djordjevic, Alex Brandner, Andreas Hörmann RegioBioFood is an application which helps people to find bio products in an easier way, only by few clicks on their android smart phone. People...

Words: 1290 - Pages: 6

Free Essay

Human Face Detection and Recognition Using Web-Cam

...Journal of Computer Science 8 (9): 1585-1593, 2012 ISSN 1549-3636 © 2012 Science Publications Human Face Detection and Recognition using Web-Cam Petcharat Pattanasethanon and Charuay Savithi Depatment of Business Computer, Faculty of Accountancy and Management, Mahasarakham UniversityKamreang, Kantharawichai, Mahasarakham 44150, Thailand Abstract: Problem statement: The illuminance insensitivity that reflects the angle of human facial aspects occurs once the distance between the object and the camera is too different such as animated images. This has been a problem for facial recognition system for decades. Approach: For this reason, our study represents a novel technique for facial recognition through the implementation of Successes Mean Quantization Transform and Spare Network of Winnow with the assistance of Eigenface computation. After having limited the frame of the input image or images from Web-Cam, the image is cropped into an oval or eclipse shape. Then the image is transformed into greyscale color and is normalized in order to reduce color complexities. We also focus on the special characteristics of human facial aspects such as nostril areas and oral areas. After every essential aspectsarescrutinized, the input image goes through the recognition system for facial identification. In some cases where the input image from the Web-Cam does not exist in the database, the user will be notified for the error handled. However, in cases where the image exists...

Words: 1996 - Pages: 8

Premium Essay

Face Recognition

...Thesis Paper Outline Format I. Introduction: In this section, give the reader an idea of why your paper will be important and/or interesting, what you will be arguing, and make the organization of the paper clear to the reader. a. Explanation of purpose and background information (optional): Explain why this topic needs to be written about (may require some background on the topic) b. Thesis statement: A basic statement of your position; your answer to your research question c. Expanded thesis statement: A brief listing of the major points that you will make in your paper, in the order in which you will make them II. Arguments: Each of your main arguments can either argue a point that supports your position, or argue against something you believe is wrong. This is a lengthy paper, so ideally you will have more than three arguments to make. You should make as many as you can. Organize your arguments to flow from one to the next or, ideally, to put your strongest arguments first and last. a. Argument 1 i. Supporting evidence (author, pg. or para. #) ii. More supporting evidence! (author, pg. or para. #) iii. Even more supporting evidence!! (author, pg. or para. #) b. Argument 2 i. Supporting evidence (author, pg. or para. #) ii. More supporting evidence! (author, pg. or para. #) iii. Even more supporting evidence!! (author, pg. or para. #) c. Argument 3 i. Supporting evidence (author, pg. or para. #) ii. More supporting evidence! (author, pg. or para...

Words: 694 - Pages: 3

Free Essay

Increased Contact Can Reduce the Other-Race Effect in Face Recognition

...Other-Race Effect in Face Recognition As humans, we come into contact with many faces in a day. The capability of these homosapiens to precisely distinguish thousands of faces is incredible seeing that all faces have approximately the similar arrangement. Nevertheless, this “gift” does not spread similarly the same to all faces. Sporer (2001) stated that humans commonly exhibit weaker remembrance for faces of another race compared to own-race faces (as cited in Hancock and Rhodes, 2008). The majority of us must have heard this line, “How am I to know if I have ever seen the person previously? They all look the same to me.” When we hear an individual say this, we’re prone to assume that the individual is racist, but is it possible that there could be a particular theory behind the notion? This occurrence is identified as the Other-Race Effect (ORE) (ORE). Tanaka, Kiefer and Bukach (2004) mentioned that the Other-Race Effect (ORE) states that individuals have a higher probability to recall and identify faces of people who are from their own race rather instead of their own racial group. Extensive evidence has proven that adults are better at distinguishing faces of their own race than those of unfamiliar races (Meissner and Brigham, 2001). The Other-Race Effect (ORE) are not stemmed from the intrinsic variances in the discriminability of diverse populations of faces. Instead, it is due to the different approaches people process own and other-race faces (Rhodes et al., 2009)...

Words: 1554 - Pages: 7

Free Essay

Ir Face Recognition

...專題製作成果報告 專題研究計畫名稱 近紅外線影像人臉辨識 組員: 姓名: 黃偉倫 學號: B9921243 姓名: 林稟軒 學號: B9921230 指導教師: 謝堯洋教授 中華民國102年6月28日 摘要 人臉辨識系統的應用非常的廣泛,人臉辨識的技術在這幾十年來也已經有不少的研究成果,因此有不少增加辨識率的方法被提出。但要如何處理當姿勢角度改變、表情改變、照片裡人臉的大小不一,甚至光所造成的亮度問題所產生的影像變化量,這是人臉辨識的一大難題。我們將針對目前提出的眾多增加辨識率之方法進行文獻探討,藉此分析比較各種人臉辨識方法之優缺點,以提供未來進行相關研究時的依據與參考,以有效增加人臉的辨識率。 而我們所做的的方面是在於紅外線的人臉辨識,而人臉辨識的步驟通常都是先把照片做處理,才會進行拿去進行辨識的工作。在紅外線人臉識別裡,有許許多多人做過了很多種方式去辨識,我們看了其中的6種辨識法來研讀,並且挑了其中的PCA、LDA、SVM來實際製作跟測試。 1. Principal Component Analysis(PCA) 2. Linear Discriminant Analysis(LDA) 3. Independent Component Analysis (ICA) 4. Support Vector Machines (SVM) 5. ARENA (基於Memory-based) 6. Unified Bayesian Framework 目錄 摘要 i 目錄 ii 第一章、緒論 1 1-1 研究動機 1 1-2 研究目的 2 第二章、理論基礎 3 2-1 研究方法 3 2-1-1 Principal Component Analysis (PCA) 3 2-1-2 Linear Discriminant Analysis (LDA) 3 2-1-3 Independent Component Analysis (ICA) 3 2-1-4 Support Vector Machines (SVM) 3 2-1-5 ARENA (基於 Memory based) 4 2-1-6 Unified Bayesian Framework 4 2-2 臉部辨識的基本過程 4 2-3 MATLAB介紹 7 第三章、實作或模擬過程 8 3-1 建立人臉資料庫以及其內部樣式 8 3-2 資料庫取樣方法 9 3-3 PCA辨識 9 3-3-1 SVD在PCA上的應用 9 3-3-2 PCA訓練方法 10 3-3-3 PCA測試方法 11 3-4 LDA辨識 11 3-5 SVM辨識 12 3-5-1 SVM訓練方法 12 3-5-2 SVM測試方法 12 3-5-3 multi SVM 12 四、測試或模擬分析結果與討論 13 4-1 測試結果 13 4-1-1 測試PCA、LDA及SVM_Linear第一階段 13 4-1-2 測試PCA、LDA及SVM_Linear第二階段 14 4-1-3 SVM影像降維度與未降維度 15 4-1-4 基於PCA第一階段之線性SVM與多項式SVM 16 4-1-5 測試結果比較圖 18 4-2 測試討論...

Words: 2058 - Pages: 9

Free Essay

Tax Research Case

...TO: J Corporation FROM: Yizhen Gong RE: §357(c) gain IRS examination DATE: October 13, 2014 Facts Joe owns 100% of the stock of J Corporation. Joe plans on contributing a parcel of land to J co. having a fair market value of $1.5 million and a basis of $350,000. Further, the land is subject to a mortgage of $1.2 million. To avoid the gain that would result on this transaction, due to §357(c), Joe plans on contributing a promissory note to J Corporation of with a face value of $850,000. He claims that this note will have a basis equal to its face value thus eliminating the gain caused by §357(c). Issues Whether Joe’s transfer of a promissory note to its wholly owned corporation, in an amount equal to the excess of liabilities over the basis of assets contributed in a §351 transfer, avoids §357(c) gain recognition? Can he increase the basis of the assets by transferring his own promissory note to the corporation? Conclusion 1. Joe received stock valued at $300,000 and thus realized a gain of $1,150,000 ($300,000+$1,200,000-$350,000). §357(c) requires Joe recognize a gain of $850,000 ($1,200,000-$$350,000). 2. It depends on whether Joe’s note has a basis in Joe’s hands for purpose of section §357(c). Analysis 1. Under §357(c), no gain or loss is if property is transferred to a corporation solely in exchange for stock of that corporation if, immediately upon the transfer, the transferors are in control of the corporation. Joe transferred...

Words: 743 - Pages: 3

Free Essay

Facebook Deepface Project

...technology is finally able to handle the extensive amounts of equations going on to create the virtual neurons necessary to recognize images and or speech. Watch Video at this link: What is DeepFace? 'Human-Level' Face Matching, Explained https://www.youtube.com/watch?v=13FZHiXJSsE Application: Although the concept of facial recognition is not a breakthrough, the uses for it are. Facebook states that the DeepFace technology will be used to improve user privacy to dispel privacy concerns it has had in the past. DeepFace will be able to identify the user in a photo and notify them before allowing them to be tagged in it without their permission. This is especially useful as it will allow the user to blur out their face in images that may be embarrassing or even incriminating. This will notify uses whether the person uploading the photo is a friend or a stranger but only allows users to see the identities of the people they are already friends. Google and the government have taken a special interest in this application. Google has developed its own variation of this technology called FaceNet that boasts more than 86% accuracy at identifying whether images are the same person, while DeepFace has an accuracy score of 97% using the same recognition data set called Labeled Faces in the Wild. The difference between the FaceNet and...

Words: 630 - Pages: 3

Premium Essay

Degree of Alignment

...* Degree of Alignment * Walgreens holds a reputation as the local trustworthy neighborhood convenient store, several which are accessible 24 hours a day, 7 days a week. This image is important to the face and ethics of the business and so we share a vision and strive to maintain its status and recognition. Walgreens wants to achieve customer satisfaction while providing everyday household items, necessities, and other merchandise. They intend to accomplish this shared vision by tending to customers ensuring satisfaction, keeping the atmosphere of the business neat and clean, and conveniently making the locations available internationally. The organization’s actual plans and actions also strive for this same goal, however, there are always other factors such as whether employees are being rude to customers, the maintenance of certain locations are not being kept, or if competition was rising causing us to lose business. * They offer services in various areas and product categories. Some of these categories include Pharmacy & Health; Photo; Beauty & Personal care; Medicine & Treatments; and Vitamins & Supplements. Other categories of interest at Walgreens include Sexual Wellness; Grocery; Baby, Kids & Toys; Household; Diet & Fitness; As Seen on TV; and seasonal products. Through providing these various products and services, they will achieve success in their goal of customer satisfaction and being the leading manufacturer for these services over...

Words: 252 - Pages: 2

Free Essay

Cutting Edge Ai

...As Nadine developes experiences in a work place she can also sharpen her skills of the task at hand. This makes Nadine a perfect AI for any work place because she will be able to adapt and even possibly assist others. The team of Nadine’s creators believe that people with autism or dementia would benefit from being around her. People who are completely alone at an old age will have a companion to talk to. B. “Sophia” * Created by Hanson Robotics at SXSW Interactive located in Austin, TX. * Two sophisticated cameras in both eyes, used to interact with humans * Tracks facial expressions and eye movement of people and also recognize them * Face made of rubbery material called “Frubber” to mimic elasticity of human face * Hanson uses a combination of Alphabet's Google Chrome voice recognition technology and other software that helps Sophia process speech, hold a conversation, remember...

Words: 845 - Pages: 4

Premium Essay

The Punch

...Punch The Punch The most important thing to remember about leaving the past behind is to never look back, no matter how tempting it might be. That means leaving the physical and mental pain with the past. They never explained how much your fist hurts after that swing. The fact that the cheekbone of boy your age is as firm as the cement floors just a few metres away. This is the easy pain though. The pain which hurts most is when your throbbing fist becomes still and the adrenaline you felt is all but a memory. I tried to argue to the ref that he was being a grub all game, that I was provoked, that he threw the first punch and that some sort of justification would ease my pain. Constant vulgarities being tossed at me, elbows to the face, rubbing my face into my own home turf ground, tasting the bitterness of the mud and feeling the dirt seeping into my pores. There’s a limit to how many times you can tell someone to back off, before the testosterone within you breaks through your self-control barrier and forces you to make sudden decisions which affect your life. So I threw my punch. Then he threw his. Exchanging rapid punches like an eager child opening a birthday present. Constant yelling from the sidelines, in the background; whether it was abusive or positive comments. We didn’t hear the words, not in our current state. Those punches and jabs were far from swift though. Still half the time they scraped past our slippery shoulders. That didn’t die down the velocity of the ‘fight’...

Words: 812 - Pages: 4

Free Essay

Eye Centre Localistaion

...ACCURATE EYE CENTRE LOCALISATION BY MEANS OF GRADIENTS Fabian Timm and Erhardt Barth Institute for Neuro- and Bioinformatics, University of L¨ beck, Ratzeburger Allee 160, D-23538 L¨ beck, Germany u u Pattern Recognition Company GmbH, Innovations Campus L¨ beck, Maria-Goeppert-Strasse 1, D-23562 L¨ beck, Germany u u {timm, barth}@inb.uni-luebeck.de Keywords: Eye centre localisation, pupil and iris localisation, image gradients, feature extraction, shape analysis. Abstract: The estimation of the eye centres is used in several computer vision applications such as face recognition or eye tracking. Especially for the latter, systems that are remote and rely on available light have become very popular and several methods for accurate eye centre localisation have been proposed. Nevertheless, these methods often fail to accurately estimate the eye centres in difficult scenarios, e.g. low resolution, low contrast, or occlusions. We therefore propose an approach for accurate and robust eye centre localisation by using image gradients. We derive a simple objective function, which only consists of dot products. The maximum of this function corresponds to the location where most gradient vectors intersect and thus to the eye’s centre. Although simple, our method is invariant to changes in scale, pose, contrast and variations in illumination. We extensively evaluate our method on the very challenging BioID database for eye centre and iris localisation. Moreover...

Words: 4275 - Pages: 18

Free Essay

Electronic Voting

...Project – an Automated Make-up color selection system. Supervisor – Dr. H.L.Premarathne Field(s) of concern – Artificial Neural Networks, Fuzzy Logic, Image Processing, Data Classification, make-up Background: Women typically like to be in the centre of attraction of other the people. In order to be elegant looking and to get the attention of others, ladies often use make-up. Make-up is a favorite topic of women, and is a primary concern, not only when attending functions such as weddings, parties, but in day-to-day life when going for work too. The success of make-up relies on how well one can select the colors that matches her skin color, eye color, shape of the face and other relevant features. Make-up is also an art; hence one should have a good artistic eye to select the make-up which suits her. Inappropriate applying of make-up will cause a person to be in the centre of sarcasm and annoyance, instead of being in the centre of attraction. This is why; ladies often take the service of a beautician. A beautician is a professional who’s trained and who has expertise knowledge on beauty therapy and make-up. With experience, a beautician can match the make-up colors to suit a person, according to her appearance and personality. However, one does not need the help of a beautician, if that person can choose the appropriate make-up colors for herself. Introduction: Selection of colors for a make-up is vital for a Beautician as well as for any lady who rely on make-up...

Words: 1003 - Pages: 5