Review of Facial Recognition and Liveness Detect

Yuxuan Wang

Abstract


Facial recognition technology has been dramatically integrated into almost all the aspects of human life, such as mobile payment, identification applications, security management, and criminal cases, etc. However, these applications can be easily fooled by deliberate spoofing strategies. To ensure the identifications of users and avoid being spoofed are the central cores of this technology. As a result, its safeness and accuracy issues attract researchers to dig into this field. In terms of present existing deception and spoofing strategies, liveness detection plays a significant role in improving the robustness of facial recognition techniques. This paper will summarize the current mainstream facial recognition technology methods. The basic ideas, methods, implementations, and corresponding drawbacks of current facial recognition methods are in this paper. The future trends of facial recognition and liveness detection are also discussed and concluded.


Keywords


Facial Recognition, Liveness Detect, Deception, Spoofing Strategy

Full Text:

PDF

Included Database


References


N. Erdogmus and S. Marcel, "Spoofing Face Recognition With 3D Masks," in IEEE Transactions on Information Forensics and Security, vol. 9, no. 7, pp. 1084-1097, July 2014.

Z. Zhu, Y. Lu and C. Chiang, "Generating Adversarial Examples By Makeup Attacks on Face Recognition," 2019 IEEE International Conference on Image Processing (ICIP), 2019.

Y. Zhong and W. Deng, "Towards Transferable Adversarial Attack Against Deep Face Recognition," in IEEE Transactions on Information Forensics and Security, vol. 16, pp. 1452-1466, 2021.

X. Lin et al., "Exploratory Adversarial Attacks on Graph Neural Networks," 2020 IEEE International Conference on Data Mining (ICDM), 2020, pp. 1136-1141.

A. K. Singh, P. Joshi and G. C. Nandi, "Face recognition with liveness detection using eye and mouth movement," 2014 International Conference on Signal Propagation and Computer Technology (ICSPCT 2014), Ajmer, 2014, pp. 592-597.

E. Jiang, "A review of the comparative studies on traditional and intelligent face recognition methods," 2020 International Conference on Computer Vision, Image and Deep Learning (CVIDL), 2020, pp. 11-15.

Huang Jiankai. Research on living detection technology of face recognition [D]. Wuhan: Central China Normal University, 2018.

W. Guojiang, Y. Guoliang and F. Kechang, "Facial Expression Recognition Based on Extended Optical Flow Constraint," 2010 International Conference on Intelligent Computation Technology and Automation, 2010, pp. 297-300.

B. K. Dehkordi and J. Haddadnia, "Facial expression recognition in video sequence images by using optical flow," 2010 2nd International Conference on Signal Processing Systems, 2010, pp. V1-727-V1-730.

B. K. Dehkordi and J. Haddadnia, "Facial expression recognition in video sequence images by using optical flow," 2010 2nd International Conference on Signal Processing Systems, 2010, pp. V1-727-V1-730.

Hu Miaochun. Robust Multispectral Features for Face Liveness Detection [D]. ejing: Beijing Jiaotong University,2015.

Liu Yifei. Face Liveness Detection Based on Spectrum Analysis and Depth Information [D]. Bejing: Beijing Jiaotong University,2017.

Yang Jianwei, Lei Zhen, Li S Z. Learn Convolutional Neural Network for Face Anti-spoofing [EB/OL]. (2014-08-26).

Wang Jiaxin and LeiZhichun. A Convolutional Neural Network Based on Feature Fusion for Face Recognition [J/OL]. Laser and Optoelectronics Progress. 2020, 57(10),339-345.

M. M. Hasan, M. S. U. Yusuf, T. I. Rohan and S. Roy, "Efficient two stage approach to detect face liveness : Motion based and Deep learning based," 2019 4th International Conference on Electrical Information and Communication Technology (EICT), 2019, pp. 1-6.

R. B. Hadiprakoso, H. Setiawan and Girinoto, "Face Anti-Spoofing Using CNN Classifier & Face liveness Detection," 2020 3rd International Conference on Information and Communications Technology (ICOIACT), 2020, pp. 143-147.

Y. Akbulut, A. Şengür, Ü. Budak and S. Ekici, "Deep learning based face liveness detection in videos," 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), 2017, pp. 1-4.

R. Koshy and A. Mahmood, "Enhanced Anisotropic Diffusion-based CNN-LSTM Architecture for Video Face Liveness Detection," 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), 2020, pp. 422-425.

Boulkenafet Z, Komulainen J, Hadid A. Face spoofing detection using colour texture analysis [J]. IEEE Trans on Information Forensics and Security, 2016, 11 (8): 1818-1830.

S. Tirunagari, N. Poh, D. Windridge, A. Iorliam, N. Suki and A. T. S. Ho, "Detection of Face Spoofing Using Visual Dynamics," in IEEE Transactions on Information Forensics and Security, vol. 10, no. 4, pp. 762-777, April 2015.

A. Lagorio, M. Tistarelli, M. Cadoni, C. Fookes and S. Sridharan, "Liveness detection based on 3D face shape analysis," 2013 International Workshop on Biometrics and Forensics (IWBF), Lisbon, 2013, pp. 1-4.

M. Killioğlu, M. Taşkiran and N. Kahraman, "Anti-spoofing in face recognition with liveness detection using pupil tracking," 2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI), Herl'any, 2017, pp. 000087-000092.

Deng Xiong, Wang Hongchun, Zhao Lijun, Wu Zhiyou, Pi Jiatian. Review of research methods for face recognition and live detection[J/OL]. Computer Application Research: 1-7[2019-12-12].




DOI: https://doi.org/10.18686/esta.v8i3.185

Refbacks

  • There are currently no refbacks.


Copyright (c) 2021 Yuxuan Wang

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.