Deep Embedding for Face Recognition in Public Video Surveillance

Face recognition is essential to the surveillance-based crime investigation. The recognition accuracy on benchmark datasets has been boosted by deep learning, while there is still large gap between academic research and practical application. This work aims to identify few suspects from the crowd in real time for public video surveillance, which is a large-scale open-set classification task. The task specific face dataset is built from security surveillance cameras in Beijng subway. The state-of-the-art deep convolutional neural networks are trained end-to-end by triplet supervisory signal to embed faces into 128-dimension feature spaces. The Euclid distances in the embedding space directly correspond to face similarity, which enables real time large scale recognition in embedded system. Experiments demonstrate a 98.92% ± 0.005 pair-wise verification accuracy, which indicates the automatic learned features are highly discriminative and generalize well to new identities. This method outperforms other state-of-the-art methods on the suspects identification task, which fills the application gap in public video surveillance.