Face Presentation Attack with Latex Masks in Multispectral Videos
Face Presentation Attack with Latex Masks in Multispectral Videos
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DOI：
10.1109/cvprw.2017.40
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发表时间：
2017-07
期刊：
影响因子：
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通讯作者：
Akshay Agarwal;Daksha Yadav;Naman Kohli;Richa Singh;Mayank Vatsa;A. Noore
中科院分区：
文献类型：
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作者：
Akshay Agarwal;Daksha Yadav;Naman Kohli;Richa Singh;Mayank Vatsa;A. Noore
人脸识别系统容易受到呈现攻击，例如打印照片攻击、重放攻击和3D掩码攻击。这些攻击主要在可见光谱中进行研究，目的是混淆或冒充个人身份。本文提出了一个独特的多光谱视频人脸数据库的人脸呈现攻击使用乳胶和纸面具。建议的多光谱乳胶面具的视频人脸呈现攻击（MLFP）数据库包含1350个视频在可见光，近红外和热光谱。由于数据库由没有任何面具以及戴着十个不同面具的受试者的视频组成，因此使用人脸识别算法在每个频谱中分析身份隐藏的效果。我们还提出了现有的演示攻击检测算法的性能建议MLFP数据库。据观察，热成像光谱是最有效的检测人脸呈现攻击。
Face recognition systems are susceptible to presentation attacks such as printed photo attacks, replay attacks, and 3D mask attacks. These attacks, primarily studied in visible spectrum, aim to obfuscate or impersonate a person's identity. This paper presents a unique multispectral video face database for face presentation attack using latex and paper masks. The proposed Multispectral Latex Mask based Video Face Presentation Attack (MLFP) database contains 1350 videos in visible, near infrared, and thermal spectrums. Since the database consists of videos of subjects without any mask as well as wearing ten different masks, the effect of identity concealment is analyzed in each spectrum using face recognition algorithms. We also present the performance of existing presentation attack detection algorithms on the proposed MLFP database. It is observed that the thermal imaging spectrum is most effective in detecting face presentation attacks.