Robust heterogeneous discriminative analysis for face recognition with single sample per person

Single sample per person face recognition is one of the most challenging problems in face recognition (FR), where only single sample per person (SSPP) is enrolled in the gallery set for training. Although the existing patch-based methods have achieved great success in FR with SSPP, they still have limitations in feature extraction and identification stages when handling complex facial variations. In this work, we propose a new patch-based method called Robust Heterogeneous Discriminative Analysis (RHDA), for FR with SSPP. To enhance the robustness against complex facial variations, we first present a new graph-based Fisher-like criterion, which incorporates two manifold embeddings, to learn heterogeneous discriminative representations of image patches. Specifically, for each patch, the Fisher-like criterion is able to preserve the reconstruction relationship of neighboring patches from the same person, while suppressing the similarities between neighboring patches from the different persons. Then, we introduce two distance metrics, i.e., patch-to-patch distance and patch-to-manifold distance, and develop a fusion strategy to combine the recognition outputs of above two distance metrics via a joint majority voting for identification. Experimental results on various benchmark datasets demonstrate the effectiveness of the proposed method.