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Blue Eyes


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This is to certify that Mr.Subhakanta Rout bearing regd. no. 0701230381 is a bona fide student of final year(7th semester), Dept. of Computer Science and Engineering, Synergy Institute Of Engineering and Technology,Dhenkanal.

This seminar on ”Blue eyes” technology presented successfully by him towards partial fulfillment of 4 year B.Tech in Computer science and Engineering of Synergy Institute of Engineering and Technology,Dhenkanal under Biju Pattanaik University of Technology.

Er.Siddharth Dash
(Seminar Guide) Dr.C.Dash H.O.D Dept.of C.S.E


Is it possible to create a computer which can interact with us as we interact each other? For example imagine in a fine morning you walk on to your computer room and switch on your computer, and then it tells you “Hey friend, good morning you seem to be a bad mood today. And then it opens your mail box and shows you some of the mails and tries to cheer you. It seems to be a fiction, but it will be the life lead by “BLUE EYES” in the very near future. The basic idea behind this technology is to give the computer the human power. We all have some perceptual abilities. That is we can understand each others feelings. For example we can understand ones emotional state by analyzing his facial expression. If we add these perceptual abilities of human to computers would enable computers to work together with human beings as intimate partners. The “BLUE EYES” technology aims at creating computational machines that have perceptual and sensory ability like those of human beings.

















Imagine yourself in a world where humans interact with computers. You are sitting in front of your personal computer that can listen, talk, or even scream aloud. It has the ability to gather information about you and interact with you through special techniques like facial recognition, speech recognition, etc. It can even understand your emotions at the touch of the mouse. It verifies your identity, feels your presents, and starts interacting with you .You ask the computer to dial to your friend at his office. It realizes the urgency of the situation through the mouse, dials your friend at his office, and establishes a connection.

Human cognition depends primarily on the ability to perceive, interpret, and integrate audio-visuals and sensoring information. Adding extraordinary perceptual abilities to computers would enable computers to work together with human beings as intimate partners. Researchers are attempting to add more capabilities to computers that will allow them to interact like humans, recognize human presents, talk, listen, or even guess their feelings.

The BLUEEYES technology aims at creating computational machines that have perceptual and sensory ability like those of human beings. It uses non-obtrusige sensing method, employing most modern video cameras and microphones to identify the user’s actions through the use of imparted sensory abilities. The machine can understand what a user wants, where he is looking at, and even realize his physical or emotional states.


One goal of human computer interaction (HCI) is to make an adaptive, smart computer system. This type of project could possibly include gesture recognition, facial recognition, eye tracking, speech recognition, etc. Another non-invasive way to obtain information about a person is through touch. People use their computers to obtain, store and manipulate data using their computer. In order to start creating smart computers, the computer must start gaining information about the user. Our proposed method for gaining user information through touch is via a computer input device, the mouse. From the physiological data obtained from the user, an emotional state may be determined which would then be related to the task the user is currently doing on the computer. Over a period of time, a user model will be built in order to gain a sense of the user's personality. The scope of the project is to have the computer adapt to the user in order to create a better working environment where the user is more productive. The first steps towards realizing this goal are described here.

Rosalind Picard (1997) describes why emotions are important to the computing community. There are two aspects of affective computing: giving the computer the ability to detect emotions and giving the computer the ability to express emotions. Not only are emotions crucial for rational decision making as Picard describes, but emotion detection is an important step to an adaptive computer system. An adaptive, smart computer system has been driving our efforts to detect a person’s emotional state. An important element of incorporating emotion into computing is for productivity for a computer user. A study (Dryer & Horowitz, 1997) has shown that people with personalities that are similar or complement each other collaborate well. Dryer (1999) has also shown that people view their computer as having a personality. For these reasons, it is important to develop computers which can work well with its user. By matching a person’s emotional state and the context of the expressed emotion, over a period of time the person’s personality is being exhibited. Therefore, by giving the computer a longitudinal understanding of the emotional state of its user, the computer could adapt a working style which fits with its user’s personality. The result of this collaboration could increase productivity for the user.

One way of gaining information from a user non-intrusively is by video. Cameras have been used to detect a person’s emotional state (Johnson, 1999). We have explored gaining information through touch. One obvious place to put sensors is on the mouse. Through observing normal computer usage (creating and editing documents and surfing the web), people spend approximately 1/3 of their total computer time touching their input device. Because of the incredible amount of time spent touching an input device, we will explore the possibility of detecting emotion through touch.


Based on Paul Ekman’s facial expression work, we see a correlation between a person’s emotional state and a person’s physiological measurements. Selected works from Ekman and others on measuring facial behaviors describe Ekman’s Facial Action Coding System (Ekman and Rosenberg, 1997). One of his experiments involved participants attached to devices to record certain measurements including pulse, galvanic skin response (GSR), temperature, somatic movement and blood pressure. He then recorded the measurements as the participants were instructed to mimic facial expressions which corresponded to the six basic emotions. He defined the six basic emotions as anger, fear, sadness, disgust, joy and surprise. From this work, Dryer (1993) determined how physiological measures could be used to distinguish various emotional states.

Six participants were trained to exhibit the facial expressions of the six basic emotions. While each participant exhibited these expressions, the physiological changes associated with affect were assessed. The measures taken were GSR, heart rate, skin temperature and general somatic activity (GSA). These data were then subject to two analyses. For the first analysis, a multidimensional scaling (MDS) procedure was used to determine the dimensionality of the data. This analysis suggested that the physiological similarities and dissimilarities of the six emotional states fit within a four dimensional model.

For the second analysis, a discriminant function analysis was used to determine the mathematic functions that would distinguish the six emotional states. This analysis suggested that all four physiological variables made significant, nonredundant contributions to the functions that distinguish the six states. Moreover, these analyses indicate that these four physiological measures are sufficient to determine reliably a person’s specific emotional state. Because of our need to incorporate these measurements into a small, non-intrusive form, we will explore taking these measurements from the hand. The amount of conductivity of the skin is best taken from the fingers. However, the other measures may not be as obvious or robust. We hypothesize that changes in the temperature of the finger are reliable for prediction of emotion. We also hypothesize the GSA can be measured by change in movement in the computer mouse. Our efforts to develop a robust pulse meter are not discussed here.


This work explores a new direction in utilizing eye gaze for computer input. Gaze tracking has long been considered as an alternative or potentially superior pointing method for computer input. We believe that many fundamental limitations exist with traditional gaze pointing. In particular, it is unnatural to overload a perceptual channel such as vision with a motor control task. We therefore propose an alternative approach, dubbed MAGIC (Manual And Gaze Input Cascaded) pointing. With such an approach, pointing appears to the user to be a manual task, used for fine manipulation and selection. However, a large portion of the cursor movement is eliminated by warping the cursor to the eye gaze area, which encompasses the target. Two specific MAGIC pointing techniques, one conservative and one liberal, were designed, analyzed, and implemented with an eye tracker we developed. They were then tested in a pilot study. This early stage exploration showed that the MAGIC pointing techniques might offer many advantages,

including reduced physical effort and fatigue as compared to traditional manual pointing, greater accuracy and naturalness than traditional gaze pointing, and possibly faster speed than manual pointing.

The pros and cons of the two techniques are discussed in light of both performance data and subjective reports.


The MAGIC pointing program takes data from both the manual input device (of any type, such as a mouse) and the eye tracking system running either on the same machine or on another machine connected via serial port. Raw data from an eye tracker can not be directly used for gaze-based interaction, due to noise from image processing, eye movement jitters, and samples taken during saccade (ballistic eye movement) periods. We experimented with various filtering techniques and found the most effective filter in our case is similar to that described. The goal of filter design in general is to make the best compromise between preserving signal bandwidth and eliminating unwanted noise. In the case of eye tracking, as Jacob argued, eye information relevant to interaction lies in the fixations. The key is to select fixation points with minimal delay. Samples collected during a saccade are unwanted and should be avoided. In designing our algorithm for picking points of fixation, we considered our tracking system speed (30 Hz), and that the MAGIC pointing techniques utilize gaze information only once for each new target, probably immediately after a saccade. Our filtering algorithm was designed to pick a fixation with minimum delay by means of selecting two adjacent points over two samples.


It is important to consider the environment in which the speech recognition system has to work. The grammar used by the speaker and accepted by the system, noise level, noise type, position of the microphone, and speed and manner of the user’s speech are some factors that may affect the quality of speech recognition .When you dial the telephone number of a big company, you are likely to hear the sonorous voice of a cultured lady who responds to your call with great courtesy saying “Welcome to company X. Please give me the extension number you want”. You pronounce the extension number, your name, and the name of person you want to contact. If the called person accepts the call, the connection is given quickly. This is artificial intelligence where an automatic call-handling system is used without employing any telephone operator.


Artificial intelligence (AI) involves two basic ideas. First, it involves studying the thought processes of human beings. Second, it deals with representing those processes via machines (like computers, robots, etc). AI is behavior of a machine, which, if performed by a human being, would be called intelligent. It makes machines smarter and more useful, and is less expensive than natural intelligence. Natural language processing (NLP) refers to artificial intelligence methods of communicating with a computer in a natural language like English. The main objective of a NLP program is to understand input and initiate action. The input words are scanned and matched against internally stored known words. Identification of a key word causes some action to be taken. In this way, one can communicate with the computer in one’s language. No special commands or computer language are required. There is no need to enter programs in a special language forcreating software.


The user speaks to the computer through a microphone, which, in used; a simple system may contain a minimum of three filters. The more the number of filters used, the higher the probability of accurate recognition. Presently, switched capacitor digital filters are used because these can be custom-built in integrated circuit form. These are smaller and cheaper than active filters using operational amplifiers. The filter output is then fed to the ADC to translate the analogue signal into digital word.

The ADC samples the filter outputs many times a second. Each sample represents different amplitude of the signal .Evenly spaced vertical lines represent the amplitude of the audio filter output at the instant of sampling. Each value is then converted to a binary number proportional to the amplitude of the sample. A central processor unit (CPU) controls the input circuits that are fed by the ADCS. A large RAM (random access memory) stores all the digital values in a buffer area. This digital information, representing the spoken word, is now accessed by the CPU to process it further. The normal speech has a frequency range of 200 Hz to 7 kHz. Recognizing a telephone call is more difficult as it has bandwidth limitation of 300 Hz to3.3 kHz.As explained earlier, the spoken words are processed by the filters and ADCs. The binary representation of each of these words becomes a template or standard, against which the future words are compared. These templates are stored in the memory.

Once the storing process is completed, the system can go into its active mode and is capable of identifying spoken words. As each word is spoken, it is converted into binary equivalent and stored in RAM. The computer then starts searching and compares the binary input pattern with the templates. t is to be noted that even if the same speaker talks the same text, there are always slight variations in amplitude or loudness of the signal, pitch, frequency difference, time gap, etc. Due to this reason, there is never a perfect match between the template and binary input word. The pattern matching process therefore uses statistical techniques and is designed to look for the best fit.

The values of binary input words are subtracted from the corresponding values in the templates. If both the values are same, the difference is zero and there is perfect match. If not, the subtraction produces some difference or error. The smaller the error, the better the match. When the best match occurs, the word is identified and displayed on the screen or used in some other manner. The search process takes a considerable amount of time, as the CPU has to make many comparisons before recognition occurs. This necessitates use of very high-speed processors. A large RAM is also required as even though a spoken word may last only a few hundred milliseconds, but the same is translated into many thousands of digital words. It is important to note that alignment of words and templates are to be matched correctly in time, before computing the similarity score.

This process, termed as dynamic time warping, recognizes that different speakers pronounce the same words at different speeds as well as elongate different parts of the same word. This is important for the speaker-independent recognizers.


Computers would have been much more powerful, had they gained perceptual and sensory abilities of the living beings on the earth. What needs to be developed is an intimate relationship between the computer and the humans. And the Simple User Interest Tracker (SUITOR) is a revolutionary approach in this direction.
By observing the Webpage a netizen is browsing, the SUITOR can help by fetching more information at his desktop. By simply noticing where the user’s eyes focus on the computer screen, the SUITOR can be more precise in determining his topic of interest. It can even deliver relevant information to a handheld device. The success lies in how much the suitor can be intimate to the user. IBM's BlueEyes research project began with a simple question, according to Myron Flickner, a manager in Almaden's USER group: Can we exploit nonverbal cues to create more effective user interfaces? One such cue is gaze—the direction in which a person is looking. Flickner and his colleagues have created some new techniques for tracking a person's eyes and have incorporated this gaze-tracking technology into two prototypes. One, called SUITOR (Simple User Interest Tracker), fills a scrolling ticker on a computer screen with information related to the user's current task. SUITOR knows where you are looking, what applications you are running, and what Web pages you may be browsing. "If I'm reading a Web page about IBM, for instance," says Paul Maglio, the Almaden cognitive scientist who invented SUITOR, "the system presents the latest stock price or business news stories that could affect IBM. If I read the headline off the ticker, it pops up the story in a browser window. If I start to read the story, it adds related stories to the ticker.

That's the whole idea of an attentive system—one that attends to what you are doing, typing, reading, so that it can attend to your information needs."

“BLUE EYES” system provides technical means for monitoring and recording the operator’s basic physiological parameters. The most important parameter is saccadic activity1, which enables the system to monitor the status of the operator’s visual attention along with head acceleration, which accompanies large displacement of the visual axis (saccades larger than 15 degrees). Complex industrial environment can create a danger of exposing the operator to toxic substances, which can affect his cardiac, circulatory and pulmonary systems.

Thus, on the grounds of lethysmographic signal taken from the forehead skin surface, the system computes heart beat rate and blood oxygenation. The BLUEEYES system checks above parameters against abnormal (e.g. a low level of blood oxygenation or a high pulse rate) or undesirable (e.g. a longer period of lowered visual attention) values and triggers user-defined alarms when necessary. Quite often in an emergency situation operators speak to themselves expressing their surprise or stating verbally the problem. Therefore, the operator’s voice, physiological parameters and an overall view of the operating room are recorded.

This helps to reconstruct the course of operators’ work and provides data for long-term analysis. This system consists of a mobile measuring device and a central analytical system. The mobile device is integrated with Bluetooth module providing wireless interface between sensors worn by the operator and the central unit. ID cards assigned to each of the operators and adequate user profiles on the central unit side provide necessary data personalization so different people can use a single mobile device.


In creating the hardware part of the DAU we built a development board, which enabled us to mount, connect and test various peripheral devices cooperating with the microcontroller.During the implementation of the DAU we needed a piece of software to establish and test Bluetooth connections. We therefore created a tool called BlueDentist. The tool provides support for controlling the currently connected Bluetooth device. Its functions are: local device management (resetting, reading local BD_ADDR, putting in Inquiry/Page and Inquiry/Page scan modes, reading the list of locally supported features and setting UART speed) and connection management (receiving and displaying Inquiry scan results, establishing ACL links, adding SCO connections, performing link authorization procedure, sending test data packets and disconnecting).To test the possibilities and performance of the remaining parts of the Project Kit (computer, camera and database software) we created BlueCapture The tool supports capturing video data from various sources (USB web-cam, industrial camera) and storing the data in the MS SQL Server database. Additionally, the application performs sound recording. After filtering and removing insignificant fragments (i.e. silence) the audio data is stored in the database. Finally, the program plays the recorded audiovisual stream. We used the software to measure database system performance and to optimize some of the SQL queries (e.g. we replaced correlated SQL queries with cursor operations). Since all the components of the application have been tested thoroughly they were reused in the final software, which additionally reduced testing time.We also created a simple tool for recording Jazz Multisensor measurements. The program reads the data using a parallel port and writes it to a file.To program the operator’s personal ID card we use a standard parallel port, as the EPROMs and the port are both TTL-compliant. A simple dialog-based application helps to accomplish the task.


1. Researchers from IBM Center in San Jose, CA, report that a number of large retailers have implemented surveillance systems that record and interpret customer movements, using software from Almaden's BlueEyes research project. BlueEyes software makes sense of what the cameras see to answer key questions for retailers, including, How many shoppers ignored a promotion? How many stopped? How long did they stay? Did their faces register boredom or delight? How many reached for the item and put it in their shopping carts? BlueEyes works by tracking pupil, eyebrow and mouth movement. When monitoring pupils, the system uses a camera and two infrared light sources placed inside the product display. One light source is aligned with the camera's focus; the other is slightly off axis. When the eye looks into the camera-aligned light, the pupil appears bright to the sensor, and the software registers the customer's attention. This is way it captures the person's income and buying preferences. BlueEyes is actively been incorporated in some of the leading retail outlets.
2. Blue Eyes can be applied in the automobile industry. By simply touching a computer input device such as a mouse, the computer system is designed to be able to determine a person's emotional state. For cars,

it could be useful to help with critical decisions like: "I know you want to get into the fast lane, but I'm afraid I can't do that. Your too upset right now" and therefore assist in driving safely.
3. We could see its use in video games where, it could give individual challenges to customers playing video games. Typically targeting commercial business.The integration of children's toys, technologies and computers is enabling new play experiences that were not commercially feasible until recently. The Intel Play QX3 Computer Microscope, the Me2Cam with Fun Fair, and the Computer Sound Morpher are commercially available smart toy products developed by the Intel Smart Toy Lab in. One theme that is common across these PC-connected toys is that users interact with them using a combination of visual, audible and tactile input & output modalities. The presentation will provide an overview of the interaction design of these products and pose some unique challenges faced by designers and engineers of such experiences targeted at novice computer users, namely young children.

4. The familiar and useful come from things we recognize. Many of our favorite things' appearance communicate their use; they show the change in their value though patina. As technologists we are now poised to imagine a world where computing objects communicate with us in-situ; where we are. We use our looks, feelings, and actions to give the computer the experience it needs to work with us. Keyboards and mice will not continue to dominate computer user interfaces. Keyboard input will be replaced in large measure by systems that know what we want and require less explicit communication. Sensors are gaining fidelity and ubiquity to record presence and actions; sensors will notice when we enter a space, sit down, lie down, pump iron, etc. Pervasive infrastructure is recording it. This talk will cover projects from the Context Aware Computing Group at MIT Media Lab.


1. Prevention from dangerous incidents
2. Minimization of -Ecological consequences
-Financial loss
-A threat to a human life
3. The reconstruction of the course of operator’s work


The nineties witnessed quantum leaps interface designing for improved man machine interactions. The BLUE EYES technology ensures a convenient way of simplifying the life by providing more delicate and user friendly facilities in computing devices. Now that we have proven the method, the next step is to improve the hardware. Instead of using cumbersome modules to gather information about the user, it will be better to use smaller and less intrusive units. The day is not far when this technology will push its way into your house hold, making you more lazy. It may even reach your hand held mobile device. Any way this is only a technological forecast.

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...Blue is the colour between violet and green on the optical spectrum of visible light. Human eyes perceive blue when observing light with a wavelength between 450 and 495 nanometres. Blues with a higher frequency and thus a shorter wavelength gradually look more violet, while those with a lower frequency and a longer wavelength gradually appear more green. Pure blue, in the middle, has a wavelength of 470 nanometres. In painting and traditional colour theory, blue is one of the three primary colours of pigments, along with red and yellow, which can be mixed to form a wide gamut of colours. Red and blue mixed together form violet, blue and yellow together form green. Blue is also a primary colour in the RGB colour model, used to create all the colours on the screen of a television or computer monitor. The modern English word blue comes from Middle English bleu or blewe, from the Old French bleu, a word of Germanic origin, related to the Old High German word blao.[2] The clear sky and the deep sea appear blue because of an optical effect known as Rayleigh scattering. When sunlight passes through the atmosphere, the blue wavelengths are scattered more widely by the oxygen and nitrogen molecules, and more blue comes to our eyes. Rayleigh scattering also explains blue eyes; there is no blue pigment in blue eyes. Distant objects appear more blue because of another optical effect called atmospheric perspective. Blue has been used for art, decoration and as a clothing dye since ancient...

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Langston Hughes

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Place by the Ocean

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Sergeant Toomey's 'Biloxi Blues'

...Biloxi Blues was written by Neil Simon as a semi-autobiographical play. The play demonstrates the conflict between Sergeant Toomey and Private Epstein through the eyes of another soldier called Eugene Jerome. The play is the second sequel in the Eugene Trilogy. Biloxi Blues the play premiered on 8th December 1984. Sergeant Toomey’s character in the play was played by Bill Sadler. Biloxi Blues the movie premiered on 25th March 1988. Sergeant Toomey’s character was played by Christopher Walken. In this essay, we will compare Sgt. Toomey character in the play and in the movie. Bill Sadler brings out Sergeant Toomey’s authentic character as an honest, complex and intimidating person. The play audience is able to see the punitive, unfairly competitive,...

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How to Be a Good Parent

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