Skip to main content
SpringerLink
Log in

Recognizing the Faces from Surveillance Video

  • Conference paper
  • First Online:
Smart Computing Techniques and Applications

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 225))

  • 645 Accesses

Abstract

The face recognition (FR) is getting extremely well known in the field of biometric due to its unique nature. In spite of the fact that the progression of FR advancement has accomplished a moderate level of improvement with deep learning strategies, still there exists some disadvantage, for example, illumination variation, change in pose angle, occlusion, masked face, and so on. The deep learning strategies will be productive only if the learning database contains a large number of images for training purpose. It is a known fact that, a large number of images are very difficult to get always, especially from village people. In order to overcome all the discussed problems, this paper proposes two different FR approaches which functions admirably with the database even with few training images. The two methods proposed are (i) face recognition with local linear regression (LLR) using a trained artificial neural network (ANN), (ii) FR with support vector machine (SVM) by utilizing the well-known particle swarm optimization (PSO) technique for optimizing the SVM kernel parameters. The performance of both the techniques is measured in terms of statistical measures and the computation speed. The performance of the proposed methods is also compared with a very recent and similar active appearance model (AAM)-based FR. From the experiment and result analysis, it is clear that the proposed PSO-SVM-based FR yields a maximum accuracy of 95%, an average accuracy of 91.5% with a minimum time requirement of 0.03 s.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Jain, A.K., Ross, A., Prabhakar, S.: An introduction to biometric recognition. In: Appeared in IEEE Transactions on Circuits and Systems for Video Technology, Special Issue on Image- and Video-Based Biometrics, vol. 14(1) (2004)

    Google Scholar 

  2. Delac, K.,Grgic, M.: A survey of biometric recognition. In: 46th International Symposium Electronics in Marine, ELMAR-2004, 16–18. Zadar, Croatia (2004)

    Google Scholar 

  3. Prabhakar, S., Pankanti, S., Jain, A.K.: Biometric recognition: security and privacy concerns. IEEE Sec. Privacy Magz. 1(2), 33–42 (2003)

    Article  Google Scholar 

  4. Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition. Springer, NY (2003)

    MATH  Google Scholar 

  5. Jain, A.K., Bolle, R., Pankanti, S.: Biometrics: Personal Identification in Networked Society. Kluwer Academic Publishers (1999)

    Google Scholar 

  6. Turk, M., Pentland, A.: Eigenfaces for recognition. Cognitive Neurosci. 3, 72–86 (1991)

    Google Scholar 

  7. Kirby, M., Sirovich, L.: Application of the Karhunen-Loeve procedure for the characterization of human faces. IEEE Trans. Pattern Anal. Mach. Intell. 12(1), 103–108 (1990)

    Article  Google Scholar 

  8. Zhao, W., Chellappa, R.: Robust face recognition using symmetric shapefrom-shading. Technical Report, Center for Automation Research, University of Maryland (1999)

    Google Scholar 

  9. Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. (CSUR) 35(4), 399–458 (2003)

    Article  Google Scholar 

  10. Wu, Y.-L., Jiao, L., Wu, G., Chang, E.Y., Wang, Y.-F.: Invariant feature extraction and biased statistical inference for video surveillance. In: Proceedings of IEEE Conference on Advanced Video and Signal Based Surveillance, pp. 284–289 (2003)

    Google Scholar 

  11. Ramanathan, N., Chellappa, R.: Face verification across age progression. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 462–469 (2005)

    Google Scholar 

  12. Chai, X., Shan, S., Chen, X., Gao, W.: Local linear regression (LLR) for pose invariant face recognition. EEE Trans. Image Process 16(7), 1716–25. https://doi.org/10.1109/tip.2007.899195

  13. Portera, G., Doran, G.: An anatomical and photographic technique for forensic facial identification. Forensic Sci. Int. 114, 97–105 (2000)

    Article  Google Scholar 

  14. Aggarwal, G., Roy-Chowdhury, A.K., Chellappa, R.: A system identification approach for video-based face recognition. Proc. Int. Conf. Pattern Recognit. 4, 175–178 (2004)

    Article  Google Scholar 

  15. Zhou, S., Krueger, V., Chellappa, R.: Probabilistic recognition of human faces from video. Comput. Vis. Image Underst. 91, 214–245 (2003)

    Article  Google Scholar 

  16. Stillman, S., Tanawongsuwan, R., Essa, I.: A system for tracking and recognizing multiple people with multiple cameras. In: Proceedings of International Conference on Audio and Video-Based Biometric Person Authentication, pp. 96–101 (1999)

    Google Scholar 

  17. Shermina, J.: Impact of locally linear regression and fisher linear discriminant analysis in pose invariant face recognition. Int. J. Comput. Sci. Netw. Sec. 10(10) 106–110 (2010)

    Google Scholar 

  18. Zhang, H., Qu, Z., Yuan, L., Li, G.: A face recognition method based on LBP feature for CNN. In: 2017 IEEE 2nd advanced information technology, electronic and automation control conference (IAEAC), pp. 544–547 (2017)

    Google Scholar 

  19. Venkatesan, S., Srinivasa Rao Madane, S.: Face recognition system with genetic algorithm and ANT colony optimization. Int. J. Innov. Manage. Technol. 1(5) (2010)

    Google Scholar 

  20. Xu, Y., Roy-Chowdhury, A., Patel, K.: Integrating illumination, motion, and shape models for robust face recognition in video. EURASIP J. Adv. Signal Process. p. 13 (2007)

    Google Scholar 

  21. Connolly, J.F., Granger, É., Sabourin, R.: An adaptive classification system for video-based face recognition, information sciences (In Press). https://doi.org/10.1016/j.ins.2010.02.026,march2010

  22. Ng, H.U.: Pose-invariant face recognition security system. Asian J. Health Informat. Sci. 1(1), 101–111 (2006)

    Google Scholar 

  23. Chai, X., Shan, S.: Locally linear regression for pose-invariantface recognition. IEEE Trans. Image Process. 16(7), 1716–1725 (2007)

    Google Scholar 

  24. Arandjelovic, O., Cipolla, R.: A Methodology for Rapid Illumination-Invariant Face Recognition Using Image Processing Filters, pp. 159–171. Elsevier (2009)

    Google Scholar 

  25. Ding, C., Tao, D.: Trunk-branch ensemble convolutional neural networks for video-based face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 1–14 (2017). https://doi.org/10.1109/TPAMI.2017.2700390

  26. Wang, K., et al.: Facial Standardization Method in Video Face Recognition System (2016)

    Google Scholar 

  27. Liu, N.: Multiple Instance Learning with Deep Instance Selection for Video-based Face Recognition (2016). In: Dhiren, P., Jayesh, D. (eds.) PCA and LDA Method with Neural Network for Primary Diagnosis of Genetic Syndrome. International Advanced Research Journal in Science, Engineering and Technology, vol. 2, issue 10 (2015). https://doi.org/10.17148/IARJSET.2015.210. ISSN (Online) 2393–8021

  28. Shreekumar, T., Karunakara, K.: Face pose and illumination normalization for unconstraint face recognition from direct interview videos. Int. J. Recent Technol. Eng. (TM), 7(6S4) 59–68 (2019). ISSN: 2277- 3878 (Online)

    Google Scholar 

  29. Shreekumar, T., Karunakara, K.: A video face recognition system with aid of support vector machine and particle swarm optimization (PSO-SVM). J. Adv. Res. Dyn. Control Syst (JARDCS) 10, 496–507 (2018)

    Google Scholar 

  30. Prasanna, K.M., Rai, C.S.: A new approach for face recognition from video sequence. In: 2018 2nd International Conference on Inventive Systems and Control (ICISC), pp. 89–95. Coimbatore (2018)

    Google Scholar 

  31. Shreekumar, T., Karunakara, K.: Active appearance model based for face recognition from surveillance video. Test Eng. Manage. 83, 6969–6981 (2020). ISSN:0193–410

    Google Scholar 

  32. Shreekumar, T.,Karunakara, K.: Face pose and blur normalization for unconstraint face recognition from video/still images. Int. J. Innov. Comput. Appl. Indersci (2020). ISSN:1751–648X

    Google Scholar 

  33. Shreekumar, T., Karunakara, K.: Identifying the faces from poor quality image/video. Int. J. Innov. Technol. Exploring Eng. (IJITEE). 8(12), 1346–1353 (2019). ISSN: 2278–3075.

    Google Scholar 

  34. Amrutha, K., Shreekumar, T.: Instant warning system to detect drivers in fatigue. Int. J. Sci. Res. (IJSR), 4(2), 791–79 (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Mangalore Institute of Technology and Engineering, Moodabidre, Mangalore, Karnataka, India

    T. Shreekumar, H. Manjunath, B. Ravinarayana & D. R. Annappa Swami

  2. Sri Siddhartha Institute of Technology, SAHE, Tumakuru, Karnataka, India

    K. Karunakara

Authors
  1. T. Shreekumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Karunakara
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H. Manjunath
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. B. Ravinarayana
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. D. R. Annappa Swami
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  2. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges (SRMGPC), Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia

    Margarita N. Favorskaya

  4. Department of Computer Science and Engineering, Vasavi College of Engineering, Hyderabad, Telangana, India

    T. Adilakshmi

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Shreekumar, T., Karunakara, K., Manjunath, H., Ravinarayana, B., Swami, D.R.A. (2021). Recognizing the Faces from Surveillance Video. In: Satapathy, S.C., Bhateja, V., Favorskaya, M.N., Adilakshmi, T. (eds) Smart Computing Techniques and Applications. Smart Innovation, Systems and Technologies, vol 225. Springer, Singapore. https://doi.org/10.1007/978-981-16-0878-0_8

Download citation

Publish with us

Policies and ethics

Search

Navigation