Robust Heterogeneous Discriminative Analysis for Single Sample Per Person Face Recognition

Single sample face recognition is one of the most challenging problems in face recognition (FR), where only one single sample per person (SSPP) is enrolled in the gallery set for training. Although patch-based methods have achieved great success in FR with SSPP, they still have significant limitations. In this work, we propose a new patch-based method, namely Robust Heterogeneous Discriminative Analysis (RHDA), to tackle FR with SSPP. Compared with the existing patch-based methods, RHDA can enhance the robustness against complex facial variations from two aspects. First, we develop a novel 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 neighboring patches from different persons. Second, we present 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 joint majority voting for identification. Experimental results on the AR and FERET benchmark datasets demonstrate the efficacy of the proposed method.