A Graph Based Unsupervised Feature Aggregation for Face Recognition

Abstract

In most of the testing dataset, the images are collected from video clips or different environment conditions, which implies that the mutual information between pairs are significantly important. To address this problem and utilize this information, in this paper, we propose a graph-based unsupervised feature aggregation method for face recognition. Our method uses the inter-connection between pairs with a directed graph approach thus refine the pair-wise scores. First, based on the assumption that all features follow Gaussian distribution, we derive a iterative updating formula of features. Second, in discrete conditions, we build a directed graph where the affinity matrix is obtained from pair-wise similarities, and filtered by a pre-defined threshold along with K-nearest neighbor. Third, the affinity matrix is used to obtain a pseudo center matrix for the iterative update process. Besides evaluation on face recognition testing dataset, our proposed method can further be applied to semi-supervised learning to handle the unlabelled data for improving the performance of the deep models. We verified the effectiveness on 5 different datasets: IJB-C, CFP, YTF, TrillionPair and IQiYi Video dataset.