[1]LIN Feng,YANG Zhongcheng,FENG Ying,et al.Binocular camera based face liveness detection with optimized scene illumination recognition[J].CAAI Transactions on Intelligent Systems,2020,15(1):160-165.[doi:10.11992/tis.201912026]
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Binocular camera based face liveness detection with optimized scene illumination recognition
CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
15
Number of periods:
2020 1
Page number:
160-165
Column:
人工智能院长论坛
Public date:
2020-01-05
- Title:
- Binocular camera based face liveness detection with optimized scene illumination recognition
- Keywords:
- face liveness detection; face anti-counterfeiting; display attack detection; identity recognition; biometric security; deep learning; convolutional neural network; PID control
- CLC:
- TP391
- DOI:
- 10.11992/tis.201912026
- Abstract:
- Face recognition is one of the most widely applied biometric identification technologies, in which face liveness detection aiming to determine whether a face is genuine or fake, is used to help face recognition systems defend against replay and print attacks, and thus ensure system security. Considering safety and economy, binocular camera based face liveness detection is most commonly adopted at present. However, due to significant variations in lighting conditions of different scenes as well as face poses, the captured face images are often of low quality, which greatly harms the performance of face liveness detection. In this paper, we propose a binocular camera based face liveness detection algorithm, which improves detection performance through optimizing scene illumination recognition. In particular, the proposed algorithm uses the cascaded PID algorithm to adjust the light sensitivity and light supplement of the camera subject to specific lighting and pose angles. It also modifies the photometric range to be within the face area in the case of backlight to ensure effectiveness of the light exposure and supplement control strategy. Extensive experiments have been conducted and the results show that the proposed model outperforms other methods by around 30% in accuracy in complex scenes, with ensured generalizability to diverse application scenes.
- References:
-
[1] WILD P, RADU P, CHEN Lulu, et al. Robust multimodal face and fingerprint fusion in the presence of spoofing attacks[J]. Pattern recognition, 2016, 50: 17–25.
[2] BOULKENAFET Z, KOMULAINEN J, HADID A. Face anti-spoofing based on color texture analysis[C]//Proceedings of 2015 IEEE International Conference on Image Processing. Quebec City, Canada, 2015: 2636–2640.
[3] PINTO A, SCHWARTZ W R, PEDRINI H, et al. Using visual rhythms for detecting video-based facial spoof attacks[J]. IEEE transactions on information forensics and security, 2015, 10(5): 1025–1038.
[4] ATOUM Y, LIU Yaojie, JOURABLOO A, et al. Face anti-spoofing using patch and depth-based CNNs[C]//Proceedings of 2017 IEEE International Joint Conference on Biometrics. Denver, USA, 2017.
[5] LIU Yaojie, JOURABLOO A, LIU Xiaoming. Learning deep models for face anti-spoofing: binary or auxiliary supervision[J]. arXiv: 1803.11097, 2018.
[6] JOURABLOO A, LIU Yaojie, LIU Xiaoming. Face de-spoofing: anti-spoofing via noise modeling[J]. arXiv: 1807.09968, 2018.
[7] LIU Yaojie, STEHOUWER J, JOURABLOO A, et al. Deep tree learning for zero-shot face anti-spoofing[J]. arXiv: 1904.02860, 2019.
[8] 陈远浩. 一种基于红外可见双目图像的活体检测方法及装置[P]. 中国: CN106372601A, 2017-02-01.
[9] HE Kaiming, ZHANG Xiangyu, REN Shaoqing. Deep residual learning for image recognition[C]//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016.
[10] SKOGESTAD S. Simple analytic rules for model reduction and PID controller tuning[J]. Journal of process control, 2003, 13(4): 291–309.
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Last Update:
1900-01-01