Towards demographic categorization using gaze analysis

Current use of gaze analysis, which is mostly restricted to eye gaze tracking for augmentative and alternative communication (AAC) medium, can benefit people afflicted with amyotrophic lateral sclerosis (ALS). This paper advances the use of gaze analysis for biometrics purposes related to gender and age demographics to benefit applications related to retail space for targeted advertising, behavioral biometrics to benefit health care, and surveillance applications. Towards that end, this paper expands on the recently introduced Gaze ANalysis Technique (GANT) for human identification to combine the length of time spent on observing patterns of interest and the scanning patterns for biometric representation with AdaBoost and super vector machines (SVM) subsequently used for biometric categorization. The experiments conducted show that while the initial results are promising further innovation and development is required to make gaze analysis a viable alternative for demographics categorization, on its own, or together with other biometrics. Further improvements on performance are expected from the derivation, extraction, and use of alternative and novel gaze driven features. This will include among others additional information that is already available about the arc features connecting the fixation points and the dynamics they encode about the roving gaze.