Deep Texture Features for Robust Face Spoofing Detection

Autor(es)

de Souza, Gustavo Botelho ^[1] ; da Silva Santos, Daniel Felipe ^[2] ; Pires, Rafael Goncalves ^[1] ; Marana, Aparecido Nilceu ^[2] ; Papa, Joao Paulo ^[2]

Afiliação do(s) autor(es)

^[1] Univ Fed Sao Carlos, CCET Exact & Technol Sci Ctr, BR-17060326 Sao Carlos, SP - Brazil

^[2] UNESP Sao Paulo State Univ, Fac Sci, Dept Comp, BR-17033360 Sao Carlos, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS; v. 64, n. 12, p. 1397-1401, DEC 2017.
• Citações Web of Science: 12

Resumo

Biometric systems are quite common in our everyday life. Despite the higher difficulty to circumvent them, nowadays criminals are developing techniques to accurately simulate physical, physiological, and behavioral traits of valid users, process known as spoofing attack. In this context, robust countermeasure methods must be developed and integrated with the traditional biometric applications in order to prevent such frauds. Despite face being a promising trait due to its convenience and acceptability, face recognition systems can be easily fooled with common printed photographs. Most of state-of-the-art antispoofing techniques for face recognition applications extract handcrafted texture features from images, mainly based on the efficient local binary patterns (LBP) descriptor, to characterize them. However, recent results indicate that high-level (deep) features are more robust for such complex tasks. In this brief, a novel approach for face spoofing detection that extracts deep texture features from images by integrating the LBP descriptor to a modified convolutional neural network is proposed. Experiments on the NUAA spoofing database indicate that such deep neural network (called LBPnet) and an extended version of it (n-LBPnet) outperform other state-of-the-art techniques, presenting great results in terms of attack detection. (AU)

• Processo FAPESP: 14/12236-1 - AnImaLS: Anotação de Imagem em Larga Escala: o que máquinas e especialistas podem aprender interagindo?
• Beneficiário: Alexandre Xavier Falcão
• Modalidade de apoio: Auxílio à Pesquisa - Temático
• Processo FAPESP: 17/05522-6 - 50th Annual International Symposium of Circuits and Systems
• Beneficiário: Aparecido Nilceu Marana
• Modalidade de apoio: Auxílio à Pesquisa - Reunião - Exterior