Abstract:
Gender classification based on the facial characteristic, has been widely studied in the literature across visible and near infrared spectrum. In this paper, we explore the applicability of extended multi-spectral imaging for the gender classification by quantifying the photometric property of the captured image. We proposed a novel scheme based on the Spectral Angle Mapper (SAM) that can effectively capture the spectral information across the multi-spectral bands that is further classified using the linear Support Vector Machine (SVM). Extensive set of experiments are carried out using a newly constructed multi-spectral face database with 78300 samples stemming from 145 subjects in six different scenarios. The obtained results show the best average classification accuracy of 93.51%, signifying the applicability of the proposed approach on the extended multi-spectral face data for robust gender classification.
