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Overview of Face Recognition Methods

  • Lingfeng FangEmail author
  • Meixia Fu
  • Songlin Sun
  • Qianhan Ran
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 550)

Abstract

With the continuous development of information technology, the demand for security and safety is gradually improving. For the consideration of security, face recognition has been studied for many decades. With the development of information technology, face recognition is widely used in our daily life, especially in security systems, information security, human-computer interaction. Researches are committed to improving the recognition accuracy and response speed of the face recognition system. The state-of-art of face recognition has been significantly improved by the appearance of deep learning. Although these systems perform well on large amounts of web collected facial data, the performance and accuracy are still limited when they are applied in actual scenarios. There is still a long way to go to improve the recognition accuracy of face recognition system in real scenarios. This paper gives a comprehensive description of a series of face recognition methods. In this paper, we introduce the definition and development of face recognition, and also indicate main challenges in this domain. Furthermore, some classical popular methods in the development of face recognition technology are described in detail. Finally, the application of face recognition technology will be introduced.

Keywords

Face recognition ASM AAM PCA Eigen face Deep learning 

References

  1. 1.
  2. 2.
    Zhou, E., Cao, Z., Yin, Q.: Naive-deep face recognition: touching the limit of LFW benchmark or not? arXiv preprint arXiv:1501.04690 (2015)
  3. 3.
    Wang, W., Shan, S., Gao, W., Cao, B., Yin, B.: An improved active shape model for face alignment. In: 2002 Proceedings of IEEE International Conference on Multimodal Interfaces, pp. 523–528. IEEE (2002)Google Scholar
  4. 4.
    Cootes, T.F., Taylor, C.J., Cooper, D.H., et al.: Active shape models-their training and application. Comput. Vis. Image Underst. 61(1), 38–59 (1995)CrossRefGoogle Scholar
  5. 5.
  6. 6.
    Edwards, G.J., Cootes, T.F., Taylor, C.J.: Face recognition using active appearance models. In: Computer Vision — ECCV 1998. Springer, Berlin, Heidelberg, pp. 581–595 (1998)Google Scholar
  7. 7.
    Joshi, A.G, Deshpande, A.S.: Review of face recognition techniques. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 5, 71–75 (2015)Google Scholar
  8. 8.
    Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1991, pp. 586–591. IEEE (1991)Google Scholar
  9. 9.
    Liu, J., et al.: Targeting Ultimate Accuracy: Face Recognition via Deep Embedding (2015)Google Scholar
  10. 10.
    Huang, G.B., Lee, H., Learned-Miller, E.: Learning hierarchical representations for face verification with convolutional deep belief networks. In: Computer Vision and Pattern Recognition, vol. 157, pp. 2518–2525. IEEE (2012)Google Scholar
  11. 11.
    Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708. IEEE (2014)Google Scholar
  12. 12.
    Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: Computer Vision and Pattern Recognition, pp. 1891–1898. IEEE (2014)Google Scholar
  13. 13.
    Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering, pp. 815–823 (2015)Google Scholar
  14. 14.
    Sun, Y., Liang, D., Wang, X., et al.: DeepID3: Face Recognition with Very Deep Neural Networks. Computer Science (2015)Google Scholar
  15. 15.
    Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Web-scale training for face identification. In: Computer Vision and Pattern Recognition, pp. 2746–2754. IEEE (2015)Google Scholar
  16. 16.
    Yi, D., Lei, Z., Liao, S., et al.: Learning face representation from scratch. In: Computer Science (2014)Google Scholar
  17. 17.
    Chen, D., Cao, X., Wen, F., Sun, J.: Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3025–3032. IEEE (2013)Google Scholar
  18. 18.
    Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical Report 07–49, University of Massachusetts, Amherst, October 2007Google Scholar
  19. 19.
    Best-Rowden, L., Han, H., Otto, C., et al.: Unconstrained face recognition: identifying a person of interest from a media collection. IEEE Trans. Inf. Forensics Secur. 9(12), 2144–2157 (2014)CrossRefGoogle Scholar
  20. 20.
    Liao, S., Lei, Z., Yi, D., et al.: A benchmark study of large-scale unconstrained face recognition. In: IEEE International Joint Conference on Biometrics, pp. 1–8. IEEE (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Lingfeng Fang
    • 1
    • 2
    • 3
    Email author
  • Meixia Fu
    • 1
    • 2
    • 3
  • Songlin Sun
    • 1
    • 2
    • 3
  • Qianhan Ran
    • 4
  1. 1.National Engineering Laboratory for Mobile Network SecurityBeijing University of Posts and TelecommunicationsBeijingChina
  2. 2.Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of EducationBeijing University of Posts and TelecommunicationsBeijingChina
  3. 3.School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationBeijingChina
  4. 4.Beijing Leimo New Media Culture and Communication Co., Ltd.BeijingChina

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