A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> E. Shape based methods <s> We present an eye tracking algorithm which is robust against variations in scale, orientation and changes of eye appearances, such as eye blinking. The locations of the eye regions in the different frames are found using template matching. The method is kept invariant for rotations and scale by exploiting temporal information and by using a codebook of eye templates the method is robust against changes in eye region appearances. The entries of the codebook are generated automatically during the tracking of the eye regions and eventually represent a distinct set of eye appearances. Classification into different eye gestures, like blinking or different gaze directions, seems possible using these automatically learned patterns. <s> BIB001 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> E. Shape based methods <s> In driver assistance system, human eye gaze direction is an important feature described some driver's situation such as distraction and fatigue. This paper proposes a method to track driver's gaze direction by using deformable template matching. The method is divided into three steps: first, identifying the face area. Second, localizing the eye area. Finally, combining the eye region model and sight algorithm to determine the driver's gaze direction. Experimental results show that this method can effectively identify human's eye and track gaze motion in a real-time running basis. <s> BIB002 </s> A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms <s> E. Shape based methods <s> In this article, a low-cost system for 2D eye gaze estimation with low-resolution webcam images is presented. Two algorithms are proposed for this purpose, one for the eye-ball detection with stable approximate pupil-center and the other one for the eye movements' direction detection. Eyeball is detected using deformable angular integral search by minimum intensity (DAISMI) algorithm. Deformable template-based 2D gaze estimation (DTBGE) algorithm is employed as a noise filter for deciding the stable movement decisions. While DTBGE employs binary images, DAISMI employs gray-scale images. Right and left eye estimates are evaluated separately. DAISMI finds the stable approximate pupil-center location by calculating the mass-center of eyeball border vertices to be employed for initial deformable template alignment. DTBGE starts running with initial alignment and updates the template alignment with resulting eye movements and eyeball size frame by frame. The horizontal and vertical deviation of eye movements through eyeball size is considered as if it is directly proportional with the deviation of cursor movements in a certain screen size and resolution. The core advantage of the system is that it does not employ the real pupil-center as a reference point for gaze estimation which is more reliable against corneal reflection. Visual angle accuracy is used for the evaluation and benchmarking of the system. Effectiveness of the proposed system is presented and experimental results are shown. <s> BIB003
These methods employ deformable templates of the eye region, using two parabolas for eye contours (Fig.7) and circle for the iris, and fitting them to an eye image BIB002 - BIB003 . The procedure is to find the similarity between the template of a chosen region with images of that region. This can be done by normalized cross-correlation, modified cross-correlation or by mean square error calculation BIB001 . If the template of pixel intensities of a region is represented by T(u,v) and I(i,j) represents that of the captured image then S(i,j) is the similarity measure between the template and image. If cross correlation is used as similarity measure, then S is given by: where < > is the average operator and < x > is the pixel-by -pixel product given by: σ is the standard deviation of the area being matched. The mean squared error similarity measure is given by: