Abstract
Face recognition suits well in situations that subjects may not cooperate, such as surveillance system, which can be deployed to track movements of a newly detected thief. In this retrieval task, the choice of face representation is highly important. The rise of Deep Learning in Computer Vision has led to the rise of deep models in face recognition, such as FaceNet, DeepFace, VGG Face B, CenterLoss C, VIPLFaceNet, ... However, when it comes to applications, which model should be chosen to ensure the balance amongst accuracy, computational cost and memory resource is still an open problem. In this work, evaluations some of state-of-the-art deep models (VGG Face B, CenterLoss C, VIPLFaceNet) were conducted under different settings and benchmark protocols to illustrate the trade-offs and draw conclusions not clearly indicated in the original works.
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Notes
- 1.
And the benchmark protocols have no official website either. However, you can download the benchmark protocols here: http://biometrics.cse.msu.edu/Publications/Databases/TIFS_SI-2014_protocols.zip.
- 2.
Be vigilant that this does not imply CenterLoss C is invariant to upside down rotation.
- 3.
References
Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. (CSUR) 35(4), 399–458 (2003)
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? Adv. Neural Inf. Process. Syst. 3320–3328 (2014)
Learned-Miller, E., Huang, G.B., RoyChowdhury, A., Li, H., Hua, G.: Labeled faces in the wild: a survey. In: Advances in Face Detection and Facial Image Analysis, pp. 189–248. Springer (2016)
Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. BMVC 1(3), 6 (2015)
Wen, Y., Zhang, K., Li, Z., Qiao, Y.: A discriminative feature learning approach for deep face recognition. In: European Conference on Computer Vision, pp. 499–515. Springer (2016)
Liu, X., Kan, M., Wu, W., Shan, S., Chen, X.: Viplfacenet: an open source deep face recognition sdk. arXiv:1609.03892 (2016)
Liao, S., Lei, Z., Yi, D., Li, S.Z.: A benchmark study of large-scale unconstrained face recognition. In: 2014 IEEE International Joint Conference on Biometrics (IJCB), pp. 1–8. IEEE (2014)
Best-Rowden, L., Han, H., Otto, C., Klare, B.F., Jain, A.K.: Unconstrained face recognition: identifying a person of interest from a media collection. IEEE Trans. Inf. Forensics Secur. 9(12), 2144–2157 (2014)
Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2005, vol. 1, pp. 539–546. IEEE (2005)
Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Deepface: closing the gap to human-level performance in face verification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701–1708 (2014)
Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: Web-scale training for face identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2746–2754 (2015)
Kumar, N., Berg, A.C., Belhumeur, P.N., Nayar, S.K.: Attribute and simile classifiers for face verification. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 365–372. IEEE (2009)
Lu, C., Tang, X.: Surpassing human-level face verification performance on LFW with gaussianface. arXiv:1404.3840 (2014)
Sun, Y., Chen, Y., Wang, X., Tang, X.: Deep learning face representation by joint identification-verification. In: Advances in Neural Information Processing Systems, pp. 1988–1996 (2014)
Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1891–1898 (2014)
Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2892–2900 (2015)
Sun, Y., Liang, D., Wang, X., Tang, X.: Deepid3: face recognition with very deep neural networks. arXiv:1502.00873 (2015)
Schrofi, F., Kalenichenko, D., Philbin, J.: Facenet: a unified embedding for face recognition and clustering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 815–823 (2015)
Hu, G., Yang, Y., Yi, D., Kittler, J., Christmas, W., Li, S.Z., Hospedales, T.: When face recognition meets with deep learning: an evaluation of convolutional neural networks for face recognition. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 142–150 (2015)
Mehdipour Ghazi, M., Kemal Ekenel, H.: A comprehensive analysis of deep learning based representation for face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 34–41 (2016)
Grgic, M., Delac, K., Grgic, S.: Scface-surveillance cameras face database. Multimedia Tools Appl. 51(3), 863–879 (2011)
Hong, S., Im, W., Ryu, J., Yang, H.S.: Sspp-dan: deep domain adaptation network for face recognition with single sample per person. arXiv:1702.04069 (2017)
Peng, Y., Gökberk, B., Spreeuwers, L., Veldhuis, R.: An evaluation of super-resolution for face recognition (2012)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556 (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. Technical Report, 07-49. University of Massachusetts, Amherst (2007)
Zhang, K., Zhang, Z., Li, Z., Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Sig. Process. Lett. 23(10), 1499–1503. ISSN: 1070-9908. https://doi.org/10.1109/LSP.2016.2603342 Oct (2016)
Mathias, M., Benenson, R., Pedersoli, M., Van Gool, L.: Face detection without bells and whistles. In: European Conference on Computer Vision, pp. 720–735. Springer (2014)
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Nguyen, V., Do, T., Nguyen, VT., Ngo, T.D., Duong, D.A. (2018). How to Choose Deep Face Models for Surveillance System?. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q. (eds) Modern Approaches for Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-76081-0_31
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