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.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
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)
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 2013
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 2011
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 2013
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 2013
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 2013
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 2015
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)
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 2015
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)
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 2014
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 2015
Ding, C., Tao, D.: Trunk-branch ensemble convolutional neural networks for video-based face recognition, July 2016. https://arxiv.org/abs/1607.05427
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
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Asra, S., Nathrao, H.S. (2020). Performance Analysis of Deep Learning Based Video Face Recognition Algorithm. In: Karrupusamy, P., Chen, J., Shi, Y. (eds) Sustainable Communication Networks and Application. ICSCN 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 39. Springer, Cham. https://doi.org/10.1007/978-3-030-34515-0_64
Download citation
DOI: https://doi.org/10.1007/978-3-030-34515-0_64
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34514-3
Online ISBN: 978-3-030-34515-0
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)