Research On Gaze Tracking Method Under Natural Light

Gaze tracking is a technique for estimating the gaze directions of participants by extracting eye movement information through electronic,optical devices and software algorithms.Gaze tracking is not only used in many fields such as human-computer interaction,driving assistance and virtual reality,but also widely used as an important tool to study human psychology and emo-tions.With the popularity of ordinary cameras,gaze tracking with a single camera under natural light has become a research hot spot.However,it is difficult to implement accurate gaze tracking algorithms because the low resolution images are captured by ordinary cameras.Therefore,in view of the key techniques of the regression-based gaze tracking method under natural light,this paper studies the eye state recognition for preprocessing,eye center localization,the calculation of the anchor point of eye vectors,the calculation of eye vectors,the construction of mapping functions and the feature of multi-threshold average-binary-connected-component-centroid.The specific research contents are as follows:1.Research on eye state recognition method of gaze tracking preprocessingThe accuracy of the eye center localization is greatly affected by the state of the open and closed eyes.Before locating the center of the eye,an eye state recognition method based on the binary image is proposed in this paper.According to the different characteristics of the iris regions in binary images of the open and closed eyes,the vertical pixel standard deviation of the iris connected component of the binary image is calculated for eye state recognition.The eye state recognition method is performed in the public video database Talking Face Video,and achieves an accuracy of 98.3%for blink recognition.2.A multi-decision iris center progressive localization methodDue to eyelid occlusion,spectacle reflection and image noise in moderate and low resolution eye images,accurate iris center localization is an extremely challenging task.Based on the analysis mentioned above,this paper proposes a multi-decision iris center progressive localization method.After the iris center is roughly located,according to the eye states of open or closed,the accurate circle fitting or regional centroid method for iris center localization is determined by different schedulable conditions.The paper combines the progressive localization of the iris center with multiple decision conditions.The paper combines the progressive positioning of the iris center with multiple schedulable condi-tions.When the iris center is roughly located,the anatomical constant of the eye is used to improve and eliminate the iteration of the active contour model.According to the color dif-ference between the iris and the sclera of the eye image,the rough position of the iris center is obtained,and the schedulable condition of the ratio of the effective area is established.when the iris center is accurately located by circle fitting,the double circle model is de-signed to improve the extraction of the left and right iris edges,and then the precise position of the iris center is obtained by the least squares circle fitting.Meanwhile,the schedulable conditions of the number of the iris edge and the size of the radius are established.Accord-ing to the dark iris in the eye image and the eye states of open or closed eye,the regional centroid is used for accurately locating the iris center.The centroid of the binary connected component in proportion to the physiological scale of the iris or the maximum area of the binary connected component is extracted for accurately locating the iris center.Under the basic requirements of real-time,the paper effectively improves the accuracy of iris center localizaiton in moderate and low resolution eye images,and the proposed method has been experimentally verified.In the normal non-manual labeling,the accuracy exceeds the best results of previous work on the most challenging BioID database of the key normalized error e<0.05.3.Research on gaze tracking method based on multi-factor stable eye vectorsGaze tracking method under natural light usually necessary to obtain reference point(anchor point,fixed point)and moving point(iris center)before calculating eye vector.The occlusion caused by the rotation of the large angle head,the influence of the shadow near the eyes,the unstable eye corner reference point caused by the changes of gaze direction,and the difference in vision caused by the two eyes,are important issues for improving the accuracy of gaze tracking in moderate and low resolution images.The paper proposes a gaze tracking method based on multi-factor stable eye vectors.The method virtualizes the relatively stable feature points such as facial contour,nose bridge and alar,and eye contour(factor)into reference points.The reference point and the moving points of both eyes(factor)are used to computed the left and right eye vectors,which they are synthesized into the final stable eye vectors by different weights(factor).The head pose angles are used to improve the regression function,which it improves the accuracy of the gaze tracking.Under the basic requirements of real-time,the paper effectively improves the accuracy of gaze tracking under natural light in moderate and low resolution images,and has been verified in public and self-built databases.Its accuracy exceeds the best results of previous work on the public database without the manual labeling,and the paper provides ideas for virtual reference points and binocular vision optimization.4.Research on gaze tracking method based on a multi-threshold average centroidThe iris center is used as the moving point of the eye vector,which it cannot charac-terize the influence of gaze changes of the eyelid and iris junction.To overcome the above problems,the paper proposes a multi-threshold average centroid gaze tracking method.By selecting a series of gray thresholds to perform binarization on the normalized eye images,the average centroid is obtained by a series of binary connected components.The vertical and horizontal of the left and right eye vectors are computed by the average centroid and the eyelid,the eye corner feature points,respectively.The final eye vectors are computed by implementing different weights in the left and right eye vectors.The multi-threshold average centroid reduces the error caused by the changes of gaze direction,improves the accuracy of the gaze tracking,and the proposed method has been verified in public and self-built databases.The accuracy of the horizontal gaze direction exceeds the best result of previous work on the public database.At the same time,this method also achieves better results for non-specific individuals.