Human Visual System Based Framework For Gender Recognition
Résumé
A face reveals a great deal of information to a perceiver including gender. Humans use specific information (cue) from a face to recognize gender. The focus of this paper is to find out this cue when the Human Visual System (HVS) decodes gender of a face. The result can be used by a Computer Vision community to develop HVS inspired framework for gender recognition. We carried out a Pyscho-visual experiment to find which face region is most correlated with gender. Eye movements of 15 observers were recorded using an eye tracker when they performed gender recognition task under controlled and free viewing condition. Analysis of the eye movement shows that the eye region is the most correlated with gender recognition. We also proposed a HVS inspired automatic gender recognition framework based on the Psycho-visual experiment. The proposed framework is tested on FERET database and is shown to achieve a high recognition accuracy.