Advertisement

Performance Analysis of Deep Learning Based Video Face Recognition Algorithm

  • Shahzadi AsraEmail author
  • Holambe Sushilkumar Nathrao
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 39)

Abstract

The identities verification in videos has many applications in area of surveillance, social media and law enforcement. The existing algorithms have obtained higher verification accuracies at equal error rate but it is very difficult to achieve higher accuracy at low false accept rate and this has become major research challenge. An efficient video face recognition system has to develop and the performance is carried out to obtain accurate face recognition from video. We propose a novel algorithm for face verification from video signal and MATLAB is used to implement and simulate proposed algorithm. The performance analysis of proposed algorithm is carried out using databases such as YouTube faces and point and shoots challenge.

Keywords

Face Features Deep learning Verification Image Recognition Extraction 

References

  1. 1.
    Barr, J.R., Bowyer, K.W., Flynn, P.J., Biswas, S.: Face recognition from video: a review. Int. J. Pattern Recogn. Artif. Intell. 26(5), 1266002 (2012)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Beveridge, J., et al.: The challenge of face recognition from digital point and-shoot cameras. In: Proceedings of IEEE Conference on Biometrics Theory, Applications and Systems, pp. 1–8, October 2013Google Scholar
  3. 3.
    Wolf, L., Hassner, T., Maoz, I.: Face recognition in unconstrained videos with matched background similarity. In: Proceedings of IEEE Conference Computer Vision and Pattern Recognition, pp. 529–534, June 2011Google Scholar
  4. 4.
    Li, H., Hua, G., Lin, Z., Brandt, J., Yang, J.: Probabilistic elastic matching for pose variant face verification. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3499–3506, June 2013Google Scholar
  5. 5.
    Méndez-Vázquez, H., Martínez-Díaz, Y., Chai, Z.: Volume structured ordinal features with background similarity measure for video face recognition. In: Proceedings of International Conference on Biometrics (ICB), pp. 1–6, June 2013Google Scholar
  6. 6.
    Wolf, L., Levy, N.: The SVM-minus similarity score for video face recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3523–3530, June 2013Google Scholar
  7. 7.
    Khan, N.M., Nan, X., Quddus, A., Rosales, E., Guan, L.: On video based face recognition through adaptive sparse dictionary. In: Proceedings of IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, pp. 1–6, May 2015Google Scholar
  8. 8.
    Li, H., Hua, G., Shen, X., Lin, Z., Brandt, J.: Eigen-PEP for video face recognition. In: Proceedings of Asian Conference on Computer Vision, pp. 17–33 (2014)CrossRefGoogle Scholar
  9. 9.
    Li, H., Hua, G.: Hierarchical-PEP model for real-world face recognition. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 4055–4064, June 2015Google Scholar
  10. 10.
    Goswami, G., Bhardwaj, R., Singh, R., Vatsa, M.: MDLFace: memorability augmented deep learning for video face recognition. In: Proceedings of IEEE International Joint Conference on Biometrics (2014)Google Scholar
  11. 11.
    Hu, J., Lu, J., Tan, Y.: Discriminative deep metric learning for face verification in the wild. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1875–1882, June 2014Google Scholar
  12. 12.
    Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2892–2900, June 2015Google Scholar
  13. 13.
    Ding, C., Tao, D.: Trunk-branch ensemble convolutional neural networks for video-based face recognition, July 2016. https://arxiv.org/abs/1607.05427
  14. 14.
    Yang, J., Ren, P., Chen, D., Wen, F., Li, H., Hua, G.: Neural aggregation network for video face recognition, March 2016. https://arxiv.org/abs/1603.05474
  15. 15.
    Tran, A.T., Hassner, T., Masi, I., Medioni, G.: Regressing robust and discriminative 3D morphable models with a very deep neural network, December 2016. https://arxiv.org/abs/1612.04904

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.TPCT’s College of EngineeringOsmanabadIndia

Personalised recommendations