Adaptive Weighted Label Propagation for Local Matching Based Face Recognition

In this paper, a new algorithm named Adaptive Weighted Label Propagation (AWLP) which explores the complementary property among sub-patterns from the same face image is proposed for local matching based face recognition. The proposed AWLP first partitions the face images into several smaller sub-images. Then, multiple similarity graphs are constructed for different sub-pattern sets. At last, in order to take correlation among different sub-patterns into account, the graphs obtained by various sub-pattern sets are combined and the procedures of label prediction and graph weight learning are integrated into a unified framework to propagate the class information of the labeled samples to unlabeled ones. Moreover, a simple yet efficient iterative update algorithm is also proposed to solve our AWLP. Extensive experiments on three face benchmark databases show that AWLP has very competitive performance with the state-of-the-art algorithms.