Heterogeneous face recognition based on modality‐independent Kernel Fisher discriminant analysis joint sparse auto‐encoder

Abstract

A novel method called modality-independent Kernel discriminant analysis joint sparse auto-encoder, for solving heterogeneous face recognition problem is proposed. A projection matrix to map multimodal data into a common feature space for representing cross-modal image data is first learnt. Then extend the model via sparse auto-encoder in an unsupervised manner with the combination of a regularisation term and a Kullback–Leiber divergence term. Different from classical approaches, this model does not require the data correspondences when collecting external cross-modal data. Thus, it is practical for real-world cross-modal classification problem. Experiments conducted on two heterogeneous face datasets demonstrate the effectiveness of the proposed approach.