Eye movements during scene understanding for biometric identification

The human eye is rich in physical and behavioral attributes that can be used for biometric identification. Eye movement is a behavioral attribute that can be collected non-intrusively for biometric identification. Usually a task oriented visual stimulus is presented to the subject and his eyes are tracked using a video camera, which are then used for biometric identification. The most common visual stimulus employed includes the moving object and free viewing. In this paper I have experimented with a novel task oriented visual stimulus i.e. scene understanding. In scene understanding the observers are instructed beforehand that they must perform a task based on the contents of the image/video that will be presented. A biometric identification system has been developed based on the eye movements extracted during scene understanding. A compact and easy to extract feature vector based on clustering of eye movements has been proposed and tested using several publicly available databases and two classification schemes. The results presented in this paper with a correct identification rate of 85.72% are quite promising. Furthermore, I also provide comparative results by implementing three commonly used feature vectors for eye movements.