Gaze Tracking. Introduction :

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1 Introduction : Gaze Tracking In 1879 in Paris, Louis Émile Javal observed that reading does not involve a smooth sweeping of the eyes along the text, as previously assumed, but a series of short stops (called fixations) and quick saccades. - wikipedia. How human beings see or perceive things is of huge importance. Gaze tracking is an important mechanism in learning difference in human behaviour. Another example being, the difference in gaze pattern of a experienced driver and a novice one. Gaze tracking is used in various applications in cognitive sciences, robotics, psychology and computers. In psychological studies it is used to measure behavioral responses, in computers as an input device and in marketing as a tool to obtain optimum location to place an advertisement. Background : A lot of research has been done in gaze tracking resulting in various methods. These methods can be broadly classified in 2 categories D Gaze tracking : In this method, 3-D point of gaze is estimated which is intersected with any screen to obtain gaze location on that screen. This method is quite robust as it requires person specific calibration only once and also it can tolerate significant head movements. It requires a stereo camera and IR light D gaze tracking: This method uses a simple webcam to capture images of eye. The eye is illuminated using IR light and pupil-glint vector (in Fig. 1)is extracted from the captured image. This vector is used to estimate the screen point by a mapping function whose constants are obtained during calibration. Drawbacks of this method is that it requires fresh calibration before each use and the user needs to keep his head very still. Fig. 1 : Parts of eye My Approach : My approach to the problem was mostly governed by the devices available.

2 1. Camera : In absence of stereo camera, I chose 2-D tracking mechanism, although, 3- D tracking mechanism is more robust. Simple webcam was also not suitable as the image quality was not good enough, I used a HD webcam. 2. IR light : IR light is required for effective extraction of pupil from images. In absence of IR light pupil is distinguishable, but, the light source(not IR) needs to be placed very close to eye and also overall illumination in the room should be low. Approach 1: Extraction of pupil and glint. First, head is detected using haar cascades and location of eye is localised. Glint extraction is fairly simple as it is a white speck in black background. Pupil extraction is very difficult is absence of IR light. I tried with various configuration of light and camera. Pupil is visible in cases when light source and camera both are kept very close to eye and illumination level of room is low. (Fig.2) Also, the pupil visibility falls off in case when person is looking sideways. Placement of light source very close to eye also effects head detection and causes sharp luminosity gradient. This configuration is not suitable for gaze tracking application. Pupil in Dark room Pupil during Day time Fig. 2. Pupil in different configurations Final Approach: Extraction of glint and using Iris as an approximation for pupil. Step1. Image is converted to grayscale and Head is detected using Haar cascades.image is cropped around approximate location of eye( Fig 3) using formula : Origin cropped image = Origin head + (0, Height head / 5.5) Width croppedimage = Width head, Height croppedimage = Height head / 3.0; This approach was chosen instead of direct eye detection because, eye detection using Haar cascades is not very accurate, resulting in eyebrows being detected as eyes often. Also, head detection is done only once, all subsequent images are cropped by the same values.

3 Fig 3: Cropped grayscale image of eye area. Step 2: Two binary images are created, one each for glint detection and iris detection. The grayscale value to used as threshold is determined during calibration. Usually for glint this value is above 225 and for iris it is less than 60. The two images are then smoothened. The window size is kept 3-5 for glint owing to small size of glint and 5-9 for iris. Fig 4. Binary image for glint Binary image for iris Fig 4 : Binary images Step3 : Glint and Iris center Extraction : Glint of only right eye is considered and similarly for iris. The stray detection along the borders or of sizes too small or too large are discarded. Actual detection is done by fitting ellipse over contour points. (Fig 5)

4 Fig 5. iris and iris center drawn in white. Glint in black Step 4 : The extracted glint and iris centers are used to compute the screen gaze point using the following eqns: V x = X iris X glint, V y = Y iris - Y glint X screen = a 0 + a 1 * V x + a 2 * V y + a 3 * V x * V y Y screen = b 0 + b 1 * V x + b 2 * V y + b 3 * V y * V y The constants a 0 a 4 and b 0 b 4 are calculated during calibration. Calibration: During this phase user is asked to look at 4 different fixed points on screen. Step 1-3 are done the same, but in step 4, glint and iris center are saved in a 4x4 matrix A. The screen points are saved in 4x1 matrix B. constants values are determined by A C = B. Implementation : I have implemented two different set of codes. One for collecting results using offline calibration and another for real time glint and iris tracking. Implementation is done in Visual studio 2008 using opencv 2.1 in C. Linux was not used because linux does not support modification of resolution of webcam. 1. Offline Code : This has 3 different codes. a. testcamera.cpp : This program does head detection and then takes sequence of images. 10 proper images (no blinking etc) are then used for results. b. checkellipse.cpp : This program is used for calibration purpose. It is used to determine the threshold values for binary images using the first 4 images. c. testchanges.cpp : This program reads all the images in sequence and does glint-iris extraction and outputs screen gaze point. 2.Real time code : a. TakeSnapshots.cpp : This program does real time glint and iris tracking. b. RealTime.cpp : This program does real time calibration and gaze tracking. Online calibration is quite tedious and has not been used for error calculation. Also, an operator is required for calibration, i.e. No self calibration. This is because opencv event handling function are not working properly in case of windows.

5 Results : Results were collected by offline calibration, i.e. User was asked to look at 10 predefined points in a pre-defined sequence. First four points were used for calculation of eqn constants and next 6 for calculating error afterwards in an offline manner. 3 different sets of images were collected. 1. Without using any support for head and 4 corners (end points of X )as calibrating points. 2. With using a few boxes as support for head and 4 corners (end points of X )as calibrating points. 3. With using a few boxes as support for head and 4 end points of + as calibrating points. Without Support -( X calibration) error in cms With Support-( X calibration ) error in cms Point Point With Support - ( + ) error in cms Point Not detected Point Point Point Not detected Avg Error Effect of support : Head movements incur large errors as is evident from higher average error in case of no support while errors are quite low in case of support. Effect of calibration point : The choice of calibration is also important. When the system is calibrated using 4 diagonal corners it covers all extremes and hence, glint and iris is extracted properly from subsequent images. Whereas, when any other points are used it leaves out the extremes. As in case 3, in 2 images glint-iris vector was not calculated. These two points were close to diagonal corners. Other Factors affecting the tracking : 1. Presence of multiple light sources result in multiple glints resulting in huge errors. 2. The position of head w.r.t camera and light source. Too close a distance reduces the area on screen which can be covered as glint quickly goes away from iris. If the distance is too large then the image quality suffers.

6 3. Blinking and position of eyelids : User is required to keep his eye open as wide as comfortably possible as larger the area of iris visible, better is its approximation to pupil. 4. Also, camera should be kept below the eye level. If not then eye lids tend to cover over large part of eye in images.

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