Face Anti-Spoofing With Deep Neural Network Distillation

Autor(es)

Li, Haoliang ^[1] ; Wang, Shiqi ^{[2, 3]} ; He, Peisong ^[4] ; Rocha, Anderson ^[5]

Afiliação do(s) autor(es)

^[1] Nanyang Technol Univ, Rapid Rich Object Search Lab, Singapore 639798 - Singapore

^[2] City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong - Peoples R China

^[3] City Univ Hong Kong, Shenzhen Inst, Shenzhen 518057 - Peoples R China

^[4] Sichuan Univ, Coll Cybersecur, Chengdu 610065 - Peoples R China

^[5] Univ Estadual Campinas, Inst Comp, BR-13084851 Campinas - Brazil

• Tipo de documento: Artigo Científico
• Fonte: IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING; v. 14, n. 5, p. 933-946, AUG 2020.
• Citações Web of Science: 0

Resumo

One challenging aspect in face anti-spoofing (or presentation attack detection, PAD) refers to the difficulty of collecting enough and representative attack samples for an application-specific environment. In view of this, we tackle the problem of training a robust PAD model with limited data in an application-specific domain. We propose to leverage data from a richer and related domain to learn meaningful features through the concept of neural network distilling. We first train a deep neural network based on reasonably sufficient labeled data in an attempt to ``teach{''} a neural network for the application-specific domain for which training samples are scarce. Subsequently, we form training sample pairs from both domains and formulate a novel optimization function by considering the cross-entropy loss, as well as maximum mean discrepancy of features and paired sample similarity embedding for network distillation. Thus, we expect to capture spoofing-specific information and train a discriminative deep neural network on the application-specific domain. Extensive experiments validate the effectiveness of the proposed scheme in face anti-spoofing setups. (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
• Modalidade de apoio: Auxílio à Pesquisa - Temático