Scenario oriented discriminant analysis for still-to-video face recognition

In the Still-to-Video (S2V) face recognition, each subject is enrolled with only few high resolution images, while the probe is video clips of complex variations. As faces present distinct characteristics under different scenarios, recognition in the original space is obviously inefficient. Therefore, in this paper, we propose a novel discriminant analysis method to learn separate mappings for different scenario patterns (still, video), and further pursue a common discriminant space for the cross-scenario samples. To maximize the intra-individual correlation of samples in the mapping space, we formulate the learning objective by incorporating the intra-class compactness and the inter-class dispersion. The gradient descend algorithm is used to get the optimal solution. Experimental results on the COX-S2V dataset demonstrate the effectiveness of the proposed method and remarkable superiority over state-of-art methods.