Multimedia Tools and Applications

, Volume 77, Issue 24, pp 31763–31785 | Cite as

Face detection approach from video with the aid of KPCM and improved neural network classifier

  • A. Vivek YoganandEmail author
  • A. Celine Kavida
  • Rukmanidevi


In recent years, the detection of a human face from the video has become an interesting research topic due to the video surveillance and other security issues. Efficient face detection from the video has become an immense need as it can provide various identity measures in the field of defense and other security-related areas. In our proposed method we have developed an efficient method of face detection to index a particular face from different video shots. The proposed method can be divided into Different modules. In the first module, human face from the video is extracted using segmentation technique. In our proposed method, we have used Kernel-based Possibilistic C-Means for segmentation purpose. The second module in our method is the feature extraction process where shape, LBP, and some geometrical features are extracted. The various shape features like area, circularity, and eccentricity are extracted. Once the feature values are extracted we track the particular face using forward tracking process. After the tracking process, we employ the classification technique. The classifier we utilized here is the improved neural network where the weights factors are optimized using the modified cuckoo search algorithm. The performance is compared with some existing works in order to prove the efficiency of our proposed method.


Face detection FCM Neural network 


  1. 1.
    AL-Allaf ONA (2014) Review of face detection systems based artificial neural networks algorithms. International Journal of Multimedia & Its Applications 6:1–16CrossRefGoogle Scholar
  2. 2.
    Atan O, Andreopoulos Y, Tekin C, van der Schaar M (2013) Bandit framework for systematic learning in wireless video-based face recognition. IEEE J Sel Topics Signal ProcessGoogle Scholar
  3. 3.
    Bhatt HS, Singh R, Vatsa M (2014) On recognizing faces in videos using clustering-based re-ranking and fusion. IEEE Trans Inf Forensics Secur 9(7)CrossRefGoogle Scholar
  4. 4.
    Chin T-J, James U, Schindler K, Suter D (2005) Face recognition from video by matching image sets. In: Proc. of Digital Image Computing: Techniques and ApplicationsGoogle Scholar
  5. 5.
    Choi JY, De Neve W, Ro YM (2010) Towards an automatic face indexing system for actor-based video services in an IPTV environment. IEEE Trans Consum Electron 56(1)Google Scholar
  6. 6.
    Choi JY, Plataniotis KN, Ro YM (2012) Face feature weighted fusion based on fuzzy membership degree for video face recognition. IEEE Transactions on Systems, Man, And Cybernetics—Part B: Cybernetics, Vol. 42(4)Google Scholar
  7. 7.
    Du M, Sankaranarayanan AC, Chellappa R (2014) Robust face recognition from multi-view videos. IEEE Trans Image Process 23(3)Google Scholar
  8. 8.
    Gou G, Huang D, Wang Y (2014) Video face recognition via combination of real-time local features and temporal–spatial cues. IET Comput Vis 8(4):347–357CrossRefGoogle Scholar
  9. 9.
    Ikeda O (2005) Estimation of speaking speed for faster face detection in video-footage. In: Proc. of IEEE International Conference on Multimedia and ExpoGoogle Scholar
  10. 10.
    Kayal S (2013) Face clustering in videos: GMM-based hierarchical clustering using spatio-temporal data. In: Proc. of 13th UK Workshop on Computational Intelligence (UKCI)Google Scholar
  11. 11.
    Lu J, Cai A, Su F (2006) A new algorithm for extracting high-resolution face image from video sequence. In: Proc. of International Conference on Computational Intelligence and Security, Vol. 2Google Scholar
  12. 12.
    Machaca Arceda VE, Fernández Fabián KM, Laguna Laura PC, Rivera Tito JJ, Gutiérrez Cáceres JC (2016) Fast face detection in violent video scenes. Electronic Notes in Theoretical Computer Science 329:5–26CrossRefGoogle Scholar
  13. 13.
    Ngo TD, Le D-D, Satoh S, Duong DA (2008) Robust face track finding in video using tracked points. In: Proc. of IEEE International Conference on Signal Image Technology and Internet Based SystemsGoogle Scholar
  14. 14.
    Pandey S (2014) Review: face detection and recognition techniques. International Journal of Computer Science and Information Technologies 5:4111–4117Google Scholar
  15. 15.
    Patil SA (2013) Face recognition: a survey. Informatics Engineering, an International Journal 1:31–41Google Scholar
  16. 16.
    Tao J, Tan Y-P (2008) Face clustering in videos using constraint propagation. In: Proc. of IEEE International Symposium on Circuits and SystemsGoogle Scholar
  17. 17.
    Tsagkatakis G, Savakis A (2009) Random projections for face detection under resource constraints. In: Proc. of 16th IEEE International Conference on Image Processing (ICIP)Google Scholar
  18. 18.
  19. 19.
    Wanga S, Zhua E, Yina J, Porikli F (2018) Video anomaly detection and localization by local motion based joint video representation and OCELM. Neurocomputing 277(14):161–175CrossRefGoogle Scholar
  20. 20.
    Yoganand AV, Kavida AC (2015) Region growing and modified neural network classifier based face detection technique from video. Int J Appl Eng Res 10(12)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • A. Vivek Yoganand
    • 1
    Email author
  • A. Celine Kavida
    • 2
  • Rukmanidevi
    • 3
  1. 1.Department of Computer Science and EngineeringJayam College of Engineering and TechnologyDharmapuriIndia
  2. 2.Department of PhysicsVeltech Multitech College of EngineeringChennaiIndia
  3. 3.Department of Computer Science and EngineeringR.M.D. Engineering CollegeTiruvallurIndia

Personalised recommendations