Academic Journal of Computing & Information Science, 2020, 3(2); doi: 10.25236/AJCIS.2020.030207.
Songnan Xi1, *, Lingbo Yang2 and Yao Zhao2
1 School of Information, Beijing Wuzi University, Beijing, China
2 Tianjin All New Intelligence Ltd, Tianjin, China
*Corresponding author e-mail: [email protected]
As one of the most promising artificial intelligence technologies, face recognition technology has been traditionally associated with security and has recently expanded into other industries such as retail, marketing, health, etc. A challenging issue for face recognition is face spoofing where imposters use a variety of fake faces in an attempt to deceive the face recognition systems. Extensive research has been conducted on face anti-spoofing algorithms and systems. Some researchers tend to design highly advanced and thus complicated algorithms assuming that regular visible light cameras are used so as to reduce peripheral hardware cost. However, implementation of such complicated algorithms may need support of quite advanced processing hardware which costs even much more than what has been saved by the cameras. Furthermore, such designs are not suitable for some practical application scenarios where the space is compact and small distributive devices without super computation strength and with low power consumption have to be used. Complicated algorithms also come with decrease in stability and generalization. An effective solution to reduce algorithms complexity is to capture images using thermal cameras which are inherently convenient for detecting various presentation attacks (PA). Sole rely on thermal cameras would need very high resolution thermal lens and thus bring the problem of much increased hardware cost. Based on extensive study on existing related work and electromagnetic theories and our experience in related practical projects and products, we proposed a practical design for face recognition with anti-spoofing. In our design, a low resolution thermal camera works together with a near infrared camera for face anti-spoofing and recognition. This design is believed to fight PA effectively and recognize faces accurately with relatively low cost and compact devices. These features add to our design's competitive strength in practical applications.
face recognition, anti spoofing, non-visible light cameras
Songnan Xi, Lingbo Yang and Yao Zhao. A Practical Design for Face Recognition with Anti-Spoofing Based on Non-Visible Light Cameras. Academic Journal of Computing & Information Science (2020), Vol. 3, Issue 2: 45-53. https://doi.org/10.25236/AJCIS.2020.030207.
 Yaojie Liu, Amin Jourabloo, Xiaoming Liu, Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision, CVPR2018
 Jiangwei Li, Yunhong Wang, Tieniu Tan, A.K.Jain. Live face detection based on the analysis of fourier spectra. In SPIE (BTHI),volume 5404, pages 296–304, 2004.
 Yaojie Liu, Joel Stehouwer, Amin Jourabloo, Xiaoming Liu. Deep Tree Learning for Zero-shot Face Anti-Spoofing, CVPR2019
 Zinelabidine Boulkenafet, Jukka Komulainen, Abdenour Hadid. Face Spoofing Detection Using Colour Texture Analysis. IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, 2016.
 Zinelabidine Boulkenafet, Jukka Komulainen, Abdenour Hadid. Face Antispoofing Using Speeded-Up Robust Features and Fisher Vector Encoding, IEEE SIGNAL PROCESSING LETTERS, 2016.
 Alex Krizhevsky, llya Sutskever, Geoffrey E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in neural information processing systems 25 (2).
 Zhenqi Xu. Learning Temporal Features Using LSTM-CNN Architecture for Face Anti-spoofing, 2015 3rd IAPR.
 Jongwoo Seo, In-Jeong Chung. Face Liveness Detection Using Thermal Face-CNN with External Knowledge. Symmetry 2019, 11, 360.
 S. Woo, J. Park, J.-Y. Lee, and I. S. Kweon, “Cbam: Convolutional block attention module,” in Proc. ECCV, 2018.
 Benjamin S. Riggan, Nathaniel J. Short, Shuowen Hu. Thermal to Visible Synthesis of Face Images using Multiple Regions. arXiv: 1803.07599
 Javier Hernandez-Ortega, Time Analysis of Pulse-based Face Anti-Spoofing in Visible and NIR, CVPR2018 workshop.
 Xudong Sun, Context Based Face Spoofing Detection Using Active Near-Infrared Images, ICPR 2016.