DEEP FEATURES EXTRACTION FOR ROBUST FINGERPRINT SPOOFING ATTACK 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, UFSCar, BR-13565905 Sao Carlos, SP - Brazil

^[2] Sao Paulo State Univ, UNESP, BR-17033360 Bauru, SP - Brazil

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH; v. 9, n. 1, p. 41-49, JAN 2019.
• Citações Web of Science: 5

Resumo

Biometric systems have been widely considered as a synonym of security. However, in recent years, malicious people are violating them by presenting forged traits, such as gelatin fingers, to fool their capture sensors (spoofing attacks). To detect such frauds, methods based on traditional image descriptors have been developed, aiming liveness detection from the input data. However, due to their handcrafted approaches, most of them present low accuracy rates in challenging scenarios. In this work, we propose a novel method for fingerprint spoofing detection using the Deep Boltzmann Machines (DBM) for extraction of high-level features from the images. Such deep features are very discriminative, thus making complicated the task of forgery by attackers. Experiments show that the proposed method outperforms other state-of-the-art techniques, presenting high accuracy regarding 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