Learning Generalized Deep Feature Representation for Face Anti-Spoofing

Autor(es)

Li, Haoliang ^[1] ; He, Peisong ^[2] ; Wang, Shiqi ^[3] ; Rocha, Anderson ^[4] ; Jiang, Xinghao ^[2] ; Kot, Alex C. ^[1]

Afiliação do(s) autor(es)

^[1] Nanyang Technol Univ, Singapore 637553 - Singapore

^[2] Shanghai Jiao Tong Univ, Sch Elect Informat & Elect Engn, Shanghai 200240 - Peoples R China

^[3] City Univ Hong Kong, Dept Comp Sci, Hong Kong, Hong Kong - Peoples R China

^[4] Univ Estadual Campinas, BR-13084851 Campinas, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: IEEE Transactions on Information Forensics and Security; v. 13, n. 10, p. 2639-2652, OCT 2018.
• Citações Web of Science: 13

Resumo

In this paper, we propose a novel framework leveraging the advantages of the representational ability of deep learning and domain generalization for face spoofing detection. In particular, the generalized deep feature representation is achieved by taking hoth spatial and temporal information into consideration, and a 3D convolutional neural network architecture tailored for the spatial-temporal input is proposed. The network is first initialized by training with augmented facial samples based on cross-entropy loss and further enhanced with a specifically designed generalization loss, which coherently serves as the regularization term. The training samples from different domains can seamlessly work together for learning the generalized feature representation by manipulating their feature distribution distances. We evaluate the proposed framework with different experimental setups using various databases. Experimental results indicate that our method can learn more discriminative and generalized information compared with the state-of-the-art methods. (AU)

• Processo FAPESP: 17/12646-3 - Déjà vu: coerência temporal, espacial e de caracterização de dados heterogêneos para análise e interpretação de integridade
• Beneficiário: Anderson de Rezende Rocha
• Linha de fomento: Auxílio à Pesquisa - Temático