Context-Aware Based Discriminative Siamese Neural Network for Face Verification

Although face recognition and verification algorithms have made great success under controlled conditions in recent years. In real-world uncontrolled application scenarios, there is a fundamental challenge that how to guarantee the discriminative ability of feature from vary inputs for face verification task. Aiming at this problem, we proposed a context-aware based discriminative siamese neural network for face verification. In fact, the structure of facial image are more stable rather than hairstyle change and wearing jewelry. Firstly we use a context-aware module to anchor facial structure information by filtering out irrelevant information. For improved discrimination, we develop a siamese network including two symmetrical branch subnetworks to learn discriminative feature by labeled triad training data. The experimental results on LFW face dataset outperform some state-of-the-art face verification methods.