Title: Robust Face Anti-Spoofing with Depth Information
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| Authors: Wang, Y; Nian, FD; Li, T; Meng, ZJ; Wang, KQ
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| Author Full Names: Wang, Yan; Nian, Fudong; Li, Teng; Meng, Zhijun; Wang, Kongqiao
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| Source: JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 49 332-337; 10.1016/j.jvcir.2017.09.002 NOV 2017
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| Language: English
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| Abstract: With the prevalence of face authentication applications, the prevention of malicious attack from fake faces such as photos or videos, i.e., face anti-spoofing, has attracted much attention recently. However, while an increasing number of works on the face anti-spoofing have been reported based on 2D RGB cameras, most of them cannot handle various attacking methods. In this paper we propose a robust representation jointly modeling 2D textual information and depth information for face anti-spoofing. The textual feature is learned from 2D facial image regions using a convolutional neural network (CNN), and the depth representation is extracted from images captured by a Kinect. A face in front of the camera is classified as live if it is categorized as live using both cues. We collected a face anti-spoofing experimental dataset with depth information, and reported extensive experimental results to validate the robustness of the proposed method. (c) 2017 Elsevier Inc. All rights reserved.
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| ISSN: 1047-3203
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| eISSN: 1095-9076
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| IDS Number: FO2MQ
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| Unique ID: WOS:000416613800027
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