Advertisement

A Survey on Face Recognition in Video Surveillance

  • V. D. Ambeth KumarEmail author
  • S. Ramya
  • H. Divakar
  • G. Kumutha Rajeswari
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

In today’s world, enormous amount of threats arises due to terrorists, criminals, thieves and also illegal access of the data from the unwanted person, etc. This leads to a lot of challenges in our daily life. With the increase in threat globally the need to deploy reliable surveillance is to increase. Video surveillance is considered to be the major breakthrough in monitoring and security. In video surveillance, the facial recognition furthermore enhances the security and defense progressively. By face recognition the probe person can be recognized more accurately, efficiently and with short time. Various methods, approaches, algorithms were available for face recognition from surveillance video. The main objective of the paper is to discuss and analyze about the various facial recognition techniques.

Keywords

Illegal access Monitoring Face recognition Video surveillance 

References

  1. 1.
    Patil SA, Deore PJ (2013) face recognition: a survey. Inform Eng Int J (IEIJ) 31–41Google Scholar
  2. 2.
    Zheng Z, Kambhamettu C (2017) Multi-level feature learning for face recognition under makeup changes. In: 12th International conference on automatic face and gesture recognition. IEEE, pp 918–923Google Scholar
  3. 3.
    Chihaoui M, Elkefi A, Bellil W, Ben Amar C (2016) A survey of 2D face recognition techniques. Computers 1–28Google Scholar
  4. 4.
    Meethongjan K, Mohamad D (2007) A summary of literature review: face recognition. In: Postgraduate annual research seminar, pp 1–12Google Scholar
  5. 5.
    Vijayakumari V (2013) face recognition techniques: a survey. World J Comput Appl Technol 41–50Google Scholar
  6. 6.
    Pang L, Ngo C-W (2015) Unsupervised celebrity face naming in web videos. IEEE Trans Multimed 17(6):854–856CrossRefGoogle Scholar
  7. 7.
    Jonathon Phillips P (2017) A cross benchmark assessment of a deep convolutional neural network for face recognition. In: IEEE 12th international conference on automatic face and gesture recognition, pp 705–710Google Scholar
  8. 8.
    Masi I, Rawls S, Medioni G, Natarajan P (2015)  Pose-aware face recognition in the wild. In: CVPR, pp 4838–4868Google Scholar
  9. 9.
    Khadhraoui T (2017) Gabor-feature based local generic representation for face recognition with single sample per person. IEEE, pp 157–160Google Scholar
  10. 10.
    Zheng Z, Kambhamettu C (2017) Multi-level feature learning for face recognition under makeup changes. In: IEEE 12th international conference on automatic face and gesture recognition, pp 918–933Google Scholar
  11. 11.
    Haghighat M, Abdel-Mottaleb M (2017) Low resolution face recognition in surveillance systems using discriminant correlation analysis. IEEE, pp 912–917Google Scholar
  12. 12.
    Hu S, Short N, Riggan BS, Chasse M, Sarfraz MS (2017) Heterogeneous face recognition: recent advances in infrared-to-visible matching. IEEE, pp 883–890Google Scholar
  13. 13.
    Tran C-K, Tseng C-D, Lee T-F (2016) Improving the face recognition accuracy under varying illumination conditions for local binary patterns and local ternary patterns based on weberface and singular value decomposition, pp 5–9Google Scholar
  14. 14.
    Vega PJS, Feitosa RQ, Quirita VHA, Happ PN (2016) Single sample face recognition from video via stacked supervised auto-encoder, pp 96–103Google Scholar
  15. 15.
    Tan S, Sun X, Chan W, Qu L, Shao L (2017) Robust face recognition with kernelized locality-sensitive group sparsity representation. IEEE, pp 1–8Google Scholar
  16. 16.
    Lu J, Tan Y-P, Wang G (2013) Discriminative multimanifold analysis for face recognition from a single training sample per person 1:39–51. IEEEGoogle Scholar
  17. 17.
    Gao S, Zhang Y, Jia K, Lu J, Zhang Y (2015) Single sample face recognition via learning deep supervised autoencoders. IEEE, pp 2108–2118Google Scholar
  18. 18.
    Feng GC, Yuen PC (1988) Variance projection function and its application to eye detection for human face recognition. Pattern Recognit Lett 19:899–906CrossRefGoogle Scholar
  19. 19.
    Nikkhouy E, Abusham EEA (2011) Facial features detection using eyes-nose template. IJCSNS Int J Comput Sci Netw Secur 87–91Google Scholar
  20. 20.
    Sadrô J, Jarudi I, Sinha P (2003) The role of eyebrows in face recognition perception, pp 285–293CrossRefGoogle Scholar
  21. 21.
    Sobotka K, Pitas I (1999) Extraction of facial regions and features using color and shape information. IEEE, pp 421–425Google Scholar
  22. 22.
    Sobottka K, Pitas I (1996) Face localization and facial feature extraction based on shape and color information. IEEE, pp 483–486Google Scholar
  23. 23.
    Ju Q (2013) Robust binary neural networks based 3D face detection and accurate faceregistration. Int J Comput Intell Syst 669–683CrossRefGoogle Scholar
  24. 24.
    Chang KI, Bowyer KW, Flynn PJ (2006) Multiple nose region matching for 3D face recognition under varying facial expression. IEEE, pp 1695–1700Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • V. D. Ambeth Kumar
    • 1
    Email author
  • S. Ramya
    • 1
  • H. Divakar
    • 1
  • G. Kumutha Rajeswari
    • 1
  1. 1.Computer Science and EngineeringPanimalar Engineering CollegeChennaiIndia

Personalised recommendations