Abstract
This paper presents initial experiments of an application of deep residual network to face recognition task. We utilize 50-layer deep neural network ResNet architecture, which was presented last year on CVPR2016. The neural network was modified and then fine-tuned for face recognition purposes. The method was trained and tested on challenging Casia-WebFace database and the results were benchmarked with a simple convolutional neural network. Our experiments of classification of closed and open subset show the great potential of residual learning for face recognition.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Yi, D., Lei, Z., Liao, S., Li, Z.: Learning face representation from scratch. CoRR (2014)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of Advances in Neural Information Processing, pp. 1106–1114 (2012)
Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708 (2014)
Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: a database for studying face recognition in unconstrained environments, pp. 07–49 (2007)
Sun, Y., Wang, X., Tang, X.: Deep learning face representation by joint identification-verification, pp. 1–9. CoRR (2014)
Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust, pp. 2892–2900. CoRR (2014)
Lu, C., Tang, X.: Surpassing human-level face verification performance on LFW with Gaussian face. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, pp. 3811–3819 (2015)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815–823 (2015)
Klare, B.F., Klein, B., Taborsky, E., Blanton, A., Cheney, J., Allen, K., Grother, P., Mah, A., Burge, M., Jain, A.K.: Pushing the frontiers of unconstrained face detection and recognition: IARPA Janus Benchmark A. In: Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1931–1939 (2015)
Masi, I., Rawls, S., Medioni, G., Natarajan, P.: Pose-aware face recognition in the wild. In: Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4838–4846 (2016)
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vis. (IJCV), pp. 211–252 (2015)
Kemelmacher-Shlizerman, I., Seitz, S.M., Miller, D., Brossard, E.: The MegaFace benchmark: 1 million faces for recognition at scale. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding (2014). arXiv preprint arXiv:1408.5093
Milborrow, S., Morkel, J., Nicolls, F.: The MUCT landmarked face database. In: Pattern Recognition Association of South Africa (2010)
Acknowledgments
This work is supported by grant of the University of West Bohemia, project No. SGS-2016-039, by Ministry of Education, Youth and Sports of Czech Republic, project No. LO1506, by Russian Foundation for Basic Research, projects No. 15-07-04415 and 16-37-60100, and by the Government of Russian, grant No. 074-U01. Moreover, access to computing and storage facilities owned by parties and projects contributing to the National Grid Infrastructure MetaCentrum provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042), is greatly appreciated.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Gruber, I., Hlaváč, M., Železný, M., Karpov, A. (2017). Facing Face Recognition with ResNet: Round One. In: Ronzhin, A., Rigoll, G., Meshcheryakov, R. (eds) Interactive Collaborative Robotics. ICR 2017. Lecture Notes in Computer Science(), vol 10459. Springer, Cham. https://doi.org/10.1007/978-3-319-66471-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-66471-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-66470-5
Online ISBN: 978-3-319-66471-2
eBook Packages: Computer ScienceComputer Science (R0)