Coupled Auto-Associative Neural Networks for Heterogeneous Face Recognition

Several models have been previously suggested for learning correlated representations between source and target modalities. In this paper, we propose a novel coupled autoassociative neural network for learning a target-to-source image representation for heterogenous face recognition. This coupled network is unique, because a cross-modal transformation is learned by forcing the hidden units (latent features) of two neural networks to be as similar as possible, while simultaneously preserving information from the input. The effectiveness of this model is demonstrated using multiple existing heterogeneous face recognition databases. Moreover, the empirical results show that the learned image representation-common latent features-by the coupled auto-associative produces competitive cross-modal face recognition results. These results are obtained by training a softmax classifier using only the latent features from the source domain and testing using only the latent features from the target domain.