Abstract
The human eye is rich in physical and behavioral attributes that can be used for automatic person recognition. The physical attributes such as the iris attracted early attention and yielded significant recognition results, but like most physical biometrics, they have several disadvantages such as intrusive acquisition, vulnerability to spoofing attacks, etc. Consequently, during the last decade the behavioral attributes extracted from human eyes have steadily gained interest from the automatic person recognition research community. In this first of its kind survey, we present the studies utilizing the behavioral attributes of human eyes for automatic person recognition. We have proposed a unique classification based on the type of stimuli used to elicit behavioral attributes. In addition, for each approach we have carefully examined the common steps involved in automatic person recognition from database acquisition, feature extraction to classification. Lastly, we also present a comparison of the recognition results obtained by each approach.