Heterogeneous face recognition based on modality‐independent Kernel Fisher discriminant analysis joint sparse auto‐encoder
Weipeng Hu, Haifeng Hu · Electronics Letters · 2016
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.