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 in the database, that image will be computed for similarity measurement using Euclidean Distance measure from the input image.
Results and Conclusion: The result of our experiment reveals that the recognition process of 150 images in the database and 10 images from the Web-Cam provides 100% accuracy in terms of recognition. The runtime in this case is at 0.04 sec.
Key word: Successes Mean Quantization Transform (SMQT), Neural Network (NN), Histogram
Equalization (HE), Local Binary Patterns (LBP)
INTRODUCTION
One of the most challenging tasks that a facial recognition retrieval model has to tackle is the efficiency to identify the accurate match with the least runtime result. From most video files, blurry lighting has made it a disadvantage to identify facial clarity
(Agarwal et al., 2010). In a constantly moving environment such as personal vehicles, only certain parts of the overall facial are captured. Therefore, it is even more demanding to develop a capture model and recognition model from Web-Cams (Zhu et al., 2010).
However, in this study, we have developed a model that provides the solution for both models to be met.
There are two critical stages for the model to follow, which are the Facial Detection stage and the Facial
Recognition stage. The purpose of this research is to develop a facial recognition model which can accurately predict or match the subject's image from our database. Input image can be either from the camera storage or the database itself. The essential parts of human facial characteristics consist of eyes, nostril shapes, mouth shapes and skin. After the input image is uploaded, we set the limitation for its edged, compute for the area of human skin, crop the facial shape into and eclipse shape, convert the color into greyscale and normalize the color to reduce color complexities (Micael et al., 2007). The image or data is then transformed using Successes Mean Quantization
Transform (SMQT) technique and match with the database referencing the similarity measurement of both images. Nonetheless, if the image does not exist in the database, the input data will be kept in the database and the system will notify the user. On the other hand, if the image exists in our database, the system will compute for similarity measurement from the
Euclidean Distance of the image exist in our database.
Crimes, homicides and assaults are literally the primary problems of various countries. In some countries, social security is not provided equally to the entire population due to the low financial support for such over-prized system (Paschalakis and Bober, 2003). Our work supports low cost and highly productive system in order to assist organizations such as government, retails, shopping centers, or even private businesses that require sufficient security provider. The other aspect that this system took advantage of is the difference in facial characteristics of human. The interesting part is that we humans have different biological characteristics that are hardly changed or implanted physically. Besides, it is a multitasking system that user does not have to physically touch the
Corresponding Author: Petcharat Pattanasethanon, Depatment of Business Computer, Faculty of Accountancy and Management,
Mahasarakham UniversityKamreang, Kantharawichai, Mahasarakham 44150, Thailand
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J. Computer Sci., 8 (9): 1585-1593, 2012 equipment or yield from doing other activities, since it will detect the subject automatically that the subject does not know that he or she is being captured. We can easily agree on the benefits of facial recognition models, however, there are always some limitations in reality that affects the flaw of the system. For instance, interrupt signals from images, low resolution images and facial reflex or expressions can cause inaccuracy in the retrieving process. These scenarios are being handled through several research approaches. In our research, the system recognizes only the images with frontal facial expression that are stored in web cameras and our standard database. algorithm using single image matching method. The model is designed to have two panels in the interface.
The control panel can be interpreted as two parts; the
Web-Cam control i/o and the processing unit. Both panels are processed individually and plausibly concurrent. The first part receives an input image through the camera, which is further described in Fig.
1a as a Startup GUI. The “Start Cam” button indicates
Web-Cam activation. The square frame is design to surround the facial area as to relocate the prospective area and separate the facial area and the background.
The square frame creates a 10-20 sec. delay for the image in order to capture the facial expression as preferred. After the “Cap CAM” button is pressed, the interrupt signal is sent to our Web-Cam, which has now stopped its task since the prospective image is obtained. As shown in Fig. 1b, this process captures only the facial part of the image and transforms its size to reduce the storing area. The size of a normalized image in our research is initially set to 100×140 pixels, as shown in Fig. 2.
The square frame that crops the entire face begins once the position of the face is set to (Sx, Sy) andthe both eyes are set to (X1,Y) and (X2,Y), as demonstrated in Fig. 2a. The width (Wi) and Height
(Hi) of the image can be computed from Eq. 1 and 2, respectively: Related works: Slight changes to the sensor and illumination insensitivity in the image are some examples of the many interactions that technically affect the entire accuracy of the pattern recognition.
Since pattern recognition can be engaged and implement to several obligation such as criminal facial matching and individual identification, a wide range of research has been conducted assist the above mentioned issues. For instance, some research implemented Histogram Equalization (HE), Local
Binary Patterns (LBP) (Lahdenoja et al., 2005) and
Modified Census Transform (MCT) (Froba and Ernst,
2004) to develop a pattern recognition model. HE is a computationally expensive operation in comparison to
LBP and MCT, however, LBP and MCT are typically restricted to only extract binary patterns in a local area.
The Successive Mean Quantization Transform (SMQT)
(Micael et al., 2007) can be viewed as a tunable tradeoff between the number of quantization levels in the result and the computational load. In this study the
SMQT is used to extract features from the local area of an image. Derivations of the sensor and illumination insensitive properties of the local SMQT features are presented. Pattern recognition in the context of appearance based face detection can be approached in several ways (Yang et al., 2002). Techniques proposed for this task are for example the Neural Network (NN)
(Rowley et al., 1998), probabilistic modelling
(Schneiderman and Kanade, 1998), cascade of boosted features (AdaBoost) (Viola and Jones, 2001), Sparse
Network of Winnows (SNoW) (Gundimada and Asari,
2005), combination of AdaBoost and SNoW (Osuna et al., 1997) and the Support Vector Machine (SVM)
(Ruan andYin, 2009).
Wi = 2∆x + | x1 = x2 | (1)
Hi = 2∆y (2)
MATERIALS AND METHODS The GUI processing unit works accordingly with the flowchart describe in Fig. 3. The second part is the identification window, which recognizes the output image by going through the database with the eclipse shape frame. Later, SMQT and Eigen face techniques are applied to the image. In order to compare the similarity measurement with the database image, our reference point is based on the value of Euclidean Distance. This will also thoroughly check if the person from the input image exists in database or not. Otherwise, the user is asked to directly add this image into the database, including the date and location which that image is found. The facial identification GUI window and flowchart diagram of the system are demonstrated as in Fig. 4 and 5, respectively.
Face detection and recognition system: No matter if Face detection and face recognition technique: In the input is a single image or a video file, the algorithm this research, we propose a facial detection and facial that pattern recognition uses is almost similar. In video recognition technique which increases the accuracy and files, matching images are processed by digitizing the preciseness of the results. The detection and recognition image frame by frame. Since pattern recognition in process are based on two renowned techniques called video files can be implemented using single image Successive Mean Quantization Transform (SMQT) and matching, in this research, we provide the matching Spare Network of Winnows (SNOW).
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J. Computer Sci., 8 (9): 1585-1593, 2012
Fig. 3: GUI Input Image Flowchart explains briefly how our GUI processes the input image
(a)
Fig. 4: GUI of Identify FaceFrom Data Base
(b)
Fig. 1: Capturing Window
Captured Image
(a)Startup
GUI
(b)
Fig. 5: Flowchart of the GUI of Identify FaceProcess
(a)
(b)
The two techniques contribute to a pattern, which store details of the facial part through data transformation and separates it from the background. The second part
Fig. 2: Facial capturing and saving to database(a)
Dimension on face (b) Limitation face sizing
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J. Computer Sci., 8 (9): 1585-1593, 2012 uses Eigenface to compute for the coefficient within special characteristics from the facial part, which different people may possess different facial structure or essential characteristics from others. nonface Can be achieved using the non-face table hx∈w , face the face table h x∈w and defining a threshold for θ. Since both tables work on the same domain, this implies that one single lookup-table:
Local SMQT feature and split up SNOW classifier:
SMQT and SNOW are initially proposed by (Micael et al., 2007) for the purpose of facial detection in a high speed manner without being affected by the surrounding error signals such as illumination insensitivity or sensor variation. SMQT extracts pixels in local area of the image. The steps are described as below.
Let x be one pixel and D(x) be set of |D(x)| = D pixels from a local area in an image. Consider the
SMQT transformation of the local area Eq. 3:
SMQTL : D ( x ) → M ( x ) h x = h nonface − h face x x
Can be created for single lookup-table classification. Let the training database contain i = 1, 2,..., N feature patches with the SMQT features Mi(x) and the corresponding classes ci (face or nonface). The nonface table and the face table can then be trained with the
Winnow Update Rule (Gundimada and Asari, 2005).
Initially both tables contain zeros. If an index in the table is addressed for the first time during training, the value (weight) on that index is set to one. There are three training parameters; the threshold γ, the promotion parameter α>1 and the demotion parameter 0