A Gabor-based network for heterogeneous face recognition

In this paper, we propose a single hidden-layer Gabor-based network for heterogeneous face recognition. The proposed input layer contains novel computational units which propagate geometrically localized input image sub-blocks to hidden nodes. The propagated pixels are then convolved with a set of Gabor kernels followed by a randomly weighted summation and a non-linear activation function operation. The output layer adopts a linear weighting scheme which can be deterministically estimated similar to that in extreme learning machine. Our experiments on three experimental scenarios using BERC visual-thermal infrared database and CASIA visual-near infrared database show promising results for the proposed network.