期刊
SENSORS
卷 19, 期 2, 页码 -出版社
MDPI
DOI: 10.3390/s19020410
关键词
visible-light camera sensor-based presentation attack detection; face recognition; spatial and temporal information; stacked convolutional neural network (CNN)-recurrent neural network (RNN); handcrafted features
资金
- National Research Foundation of Korea (NRF) - Korea government (Ministry of Science and ICT) [NRF-2017R1C1B5074062]
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [NRF-2018R1D1A1B07041921]
- Bio & Medical Technology Development Program of the NRF - Korean government, MSIT [NRF-2016M3A9E1915855]
Face-based biometric recognition systems that can recognize human faces are widely employed in places such as airports, immigration offices, and companies, and applications such as mobile phones. However, the security of this recognition method can be compromised by attackers (unauthorized persons), who might bypass the recognition system using artificial facial images. In addition, most previous studies on face presentation attack detection have only utilized spatial information. To address this problem, we propose a visible-light camera sensor-based presentation attack detection that is based on both spatial and temporal information, using the deep features extracted by a stacked convolutional neural network (CNN)-recurrent neural network (RNN) along with handcrafted features. Through experiments using two public datasets, we demonstrate that the temporal information is sufficient for detecting attacks using face images. In addition, it is established that the handcrafted image features efficiently enhance the detection performance of deep features, and the proposed method outperforms previous methods.
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