Gender recognition from visible and thermal infrared facial images

Most present research of gender recognition focuses on visible facial images, which are sensitive to illumination changes. In this paper, we proposed hybrid methods for gender recognition by fusing visible and thermal infrared images. First, the active appearance model is used to extract features from visible images, as well as local binary pattern features and several statistical temperature features are extracted from thermal infrared images. Then, feature selection is performed by using the F-test statistic. Third, we propose using Bayesian Networks to perform explicit and implicit fusion of visible and thermal infrared image features. For explicit fusion, we propose two Bayesian Networks to perform decision-level and feature-level fusion. For implicit fusion, we propose using features from one modality as privileged information to improve gender recognition by another modality. Finally, we evaluate the proposed methods on the Natural Visible and Infrared facial Expression spontaneous database and the Equinox face database. Experimental results show that both feature-level and decision-level fusion improve the gender recognition performance, compared to that achieved from one modality. The proposed implicit fusion methods successfully capture the role of privileged information of one modality, thus enhance the gender recognition from another modality.