BLUE EYE TECHNOLOGY
INTRODUCTION
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. The BLUE EYES technology aims at creating computational machines that have perceptual and sensory ability like those of human beings. It uses non-obtrusive sensing method, employing most modern video cameras and microphones to identifies the users 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.
EMOTION MOUSE
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.
EXPERIMENTAL DESIGN
An experiment was designed to test the above hypotheses. The four physiological readings measured were heart rate, temperature, GSR and somatic movement. The heart rate was measured through a commercially available chest strap sensor. The temperature was measured with a thermocouple attached to a digital multimeter (DMM). The GSR was also measured with a DMM. The somatic movement was measured by recording the computer mouse movements.
Method
Six people participated in this study (3 male, 3 female). The experiment was within subject design and order of presentation was counter-balanced across participants.
Procedure
Participants were asked to sit in front of the computer and hold the temperature and GSR sensors in their left hand hold the mouse with their right hand and wore the chest sensor. The resting (baseline) measurements were recorded for five minutes and then the participant was instructed to act out one emotion for five minutes. The emotions consisted of: anger, fear, sadness, disgust, happiness and surprise. The only instruction for acting out the emotion was to show the emotion in their facial expressions.
Results
The data for each subject consisted of scores for four physiological assessments [GSA, GSR, pulse, and skin temperature, for each of the six emotions (anger, disgust, fear, happiness, sadness, and surprise)] across the five minute baseline and test sessions. GSA data was sampled 80 times per second, GSR and temperature were reported approximately 3-4 times per second and pulse was recorded as a beat was detected, approximately 1 time per second. We first calculated the mean score for each of the baseline and test sessions. To account for individual variance in physiology, we calculated the difference between the baseline and test scores. Scores that differed by more than one and a half standard deviations from the mean were treated as missing. By this criterion, twelve score were removed from the analysis. The remaining data are described in Table. In order to determine whether our measures of physiology could discriminate among the six different emotions, the data were analyzed with a discriminate function analysis. The four physiological difference scores were the discriminating variables and the six emotions were the discriminated groups. The variables were entered into the equation simultaneously, and four canonical discriminant functions were calculated. A Wilks' Lambda test of these four functions was marginally statistically significant; for lambda = .192, chi-square (20) = 29.748, p < .075. The functions are shown in Table
The unstandardized canonical discriminant functions evaluated at group means are shown in Table 3. Function 1 is defined by sadness and fear at one end and anger and surprise at the other. Function 2 has fear and disgust at one end and sadness at the other. Function 3 has happiness at one end and surprise at the other. Function 4 has disgust and anger at one end and surprise at the other. Table 3: To determine the effectiveness of these functions, we used them to predict the group membership for each set of physiological data. As shown in Table 4, two-thirds of the cases were successfully classified. The results show the theory behind the Emotion mouse work is fundamentally sound. The physiological measurements were correlated to emotions using a correlation model. The correlation model is derived from a calibration process in which a baseline attribute-to emotion correlation is rendered based on statistical analysis of calibration signals generated by users having emotions that are measured or otherwise known at calibration time.
MANUAL AND GAZE INPUT CASCADED (MAGIC) POINTING
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. In our view, there are two fundamental shortcomings to the existing gaze pointing techniques, regardless of the maturity of eye tracking technology. First, given the one-degree size of the fovea and the subconscious jittery motions that the eyes constantly produce, eye gaze is not precise enough to operate UI widgets such as scrollbars, hyperlinks, and slider handles In Proc. CHI'99: ACM Conference on Human Factors in Computing Systems. 246-253, Pittsburgh, 15-20 May1999 Copyright ACM 1999 0-201-48559-1/99/05...$5.00 on today's GUI interfaces. At a 25-inch viewing distance to the screen, one degree of arc corresponds to 0.44 in, which is twice the size of a typical scroll bar and much greater than the size of a typical character.
The liberal MAGIC pointing technique: cursor is placed in the vicinity of a target that the user fixates on. Actuate input device, observe the cursor position and decide in which direction to steer the cursor. The cost to this method is the increased manual movement amplitude. Figure 2. The conservative MAGIC pointing technique with "intelligent offset" To initiate a pointing trial, there are two strategies available to the user. One is to follow "virtual inertia:" move from the cursor's current position towards the new target the user is looking at. This is likely the strategy the user will employ, due to the way the user interacts with today's interface.
1. With the more liberal MAGIC pointing technique, the cursor warping can be overactive at times, since the cursor moves to the new gaze location whenever the eye gaze moves more than a set distance (e.g., 120 pixels) away from the cursor. This could be particularly distracting when the user is trying to read. It is possible to introduce additional constraint according to the context. For example, when the user's eye appears to follow a text reading pattern, MAGIC pointing can be automatically suppressed.
2. With the more conservative MAGIC pointing technique, the uncertainty of the exact location at which the cursor might appear may force the user, especially a novice, to adopt a cumbersome strategy: take a touch (use the manual input device to activate the cursor), wait (for the cursor to appear), and move (the cursor to the target manually). Such a strategy may prolong the target acquisition time. The user may have to learn a novel hand-eye coordination pattern to be efficient with this technique. Gaze position reported by eye tracker Eye tracking boundary with 95% confidence True target will be within the circle with 95% probability The cursor is warped to the boundary of the gaze area, along the initial actuation vector Previous cursor position, far from target Initial manual actuation vector.
3. With pure manual pointing techniques, the user, knowing the current cursor location, could conceivably perform his motor acts in parallel to visual search. Motor action may start as soon as the user's gaze settles on a target. With MAGIC pointing techniques, the motor action computation (decision) cannot start until the cursor appears. This may negate the time saving gained from the MAGIC pointing technique's reduction of movement amplitude. Clearly, experimental (implementation and empirical) work is needed to validate, refine, or invent alternative MAGIC pointing techniques.
IMPLEMENTATION
We took two engineering efforts to implement the MAGIC pointing techniques. One was to design and implement an eye tracking system and the other was to implement MAGIC pointing techniques at the operating systems level, so that the techniques can work with all software applications beyond "demonstration" software.
THE IBM ALMADEN EYE TRACKER
Since the goal of this work is to explore MAGIC pointing as a user interface technique, we started out by purchasing a commercial eye tracker (ASL Model 5000) after a market survey. In comparison to the system reported in early studies (e.g. [7]), this system is much more compact and reliable. However, we felt that it was still not robust enough for a variety of people with different eye characteristics, such as pupil brightness and correction glasses. We hence chose to develop and use our own eye tracking system [10]. Available commercial systems, such as those made by ISCAN Incorporated, LC Technologies, and Applied Science Laboratories (ASL), rely on a single light source that is positioned either off the camera axis in the case of the ISCANETL-400 systems, or on-axis in the case of the LCT and the ASL E504 systems. Illumination from an off-axis source (or ambient illumination) generates a dark pupil image. When the light source is placed on-axis with the camera optical axis, the camera is able to detect the light reflected from the interior of the eye, and the image of the pupil appears bright.
Bright (left) and dark (right) pupil images resulting from on- and off-axis illumination. The glints, or corneal reflections, from the on- and off-axis light sources can be easily identified as the bright points in the iris. The Almaden system uses two near infrared (IR) time multiplexed light sources, composed of two sets of IR LED's, which were synchronized with the camera frame rate. One light source is placed very close to the camera's optical axis and is synchronized with the even frames. Odd frames are synchronized with the second light source, positioned off axis. The two light sources are calibrated to provide approximately equivalent whole-scene illumination. Pupil detection is realized by means of subtracting the dark pupil image from the bright pupil image. After thresholding the difference, the largest connected component is identified as the pupil. This technique significantly increases the robustness and reliability of the eye tracking system. After implementing our system with satisfactory results, we discovered that similar pupil detection schemes had been independently developed by Tomonoetal and Ebisawa and Satoh.
IMPLIMENTING MAGIC POINTING
We programmed the two MAGIC pointing techniques on a Windows NT system. The techniques work independently from the applications. 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 in [7]. 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.
EXPERIMENT
Empirical studies, are relatively rare in eye tracking-based interaction research, although they are particularly needed in this field. Human behavior and processes at the perceptual motor level often do not conform to conscious-level reasoning. One usually cannot correctly describe how to make a turn on a bicycle. Hypotheses on novel interaction techniques can only be validated by empirical data. However, it is also particularly difficult to conduct empirical research on gaze-based interaction techniques, due to the complexity of eye movement and the lack of reliability in eye tracking equipment.
EXPERIMENTAL DESIGN
The two MAGIC pointing techniques described earlier were put to test using a set of parameters such as the filter's temporal and spatial thresholds, the minimum cursor warping distance, and the amount of "intelligent bias" (subjectively selected by the authors without extensive user testing). Ultimately the MAGIC pointing techniques should be evaluated with an array of manual input devices, against both pure manual and pure gaze-operated pointing methods. Since this is an early pilot study, we decided to limit ourselves to one manual input device. A standard mouse was first considered to be the manual input device in the experiment. However, it was soon realized not to be the most suitable device for MAGIC pointing, especially when a user decides to use the push-upwards strategy with the intelligent offset. Because in such a case the user always moves in one direction, the mouse tends to be moved off the pad, forcing the user adjust the mouse position, often during a pointing trial. We hence decided to use a miniature isometric pointing stick (IBM Track Point IV, commercially used in the IBM ThinkPad 600 and 770 series notebook computers). Another device suitable for MAGIC pointing is a touchpad: the user can choose one convenient gesture and to take advantage of the intelligent offset. The experimental task was essentially a Fitts' pointing task. Subjects were asked to point and click at targets appearing in random order. If the subject clicked off-target, a miss was logged but the trial continued until a target was clicked. An extra trial was added to make up for the missed trial. Only trials with no misses were collected for time performance analyses. Subjects were asked to complete the task as quickly as possible and as accurately as possible. To serve as a motivator, a $20 cash prize was set for the subject with the shortest mean session completion time with any technique.
The task was presented on a 20 inch CRT color monitor, with a 15 by 11 inch viewable area set at resolution of 1280 by 1024 pixels. Subjects sat from the screen at a distance of 25 inches. The following factors were manipulated in the experiments:
two target sizes: 20 pixels (0.23 in or 0.53 degree of viewing angle at 25 in distance) and 60 pixels in diameter (0.7 in, 1.61 degree)
three target distances: 200 pixels (2.34 in, 5.37 degree), 500 pixels (5.85 in, 13.37 degree), and 800 pixels (9.38 in, 21.24 degree)
three pointing directions: horizontal, vertical and diagonal
A within-subject design was used. Each subject performed the task with all three techniques: (1) Standard, pure manual pointing with no gaze tracking (No Gaze); (2) The conservative MAGIC pointing method with intelligent offset (Gaze1); (3) The liberal MAGIC pointing method (Gaze2). Nine subjects, seven male and two female, completed the experiment. The order of techniques was balanced by a Latin square pattern. Seven subjects were experienced Track Point users, while two had little or no experience. With each technique, a 36-trial practice session was first given, during which subjects were encouraged to explore and
to find the most suitable strategies (aggressive, gentle, etc.). The practice session was followed by two data collection sessions. Although our eye tracking system allows head motion, at least for those users who do not wear glasses, we decided to use a chin rest to minimize instrumental error.
ARTIFICIAL INTELLIGENT SPEECH RECOGNITION
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.
CONCLUSION
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.
