Abstract
This paper proposes a compact face representation for face recognition. The face with landmark points in the image is detected and then used to generate transformed face regions. Different types of regions form the transformed face region datasets, and face networks are trained. A novel forward model selection algorithm is designed to simultaneously select the complementary face models and generate the compact representation. Employing a public dataset as training set and fusing by only six selected face networks, the recognition system with this compact face representation achieves 99.05 % accuracy on LFW benchmark.
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Notes
- 1.
The performance is the cross-validation result on CASIA-WebFace, which has a same order with the result of LFW.
References
Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)
Liu, C., Wechsler, H.: Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans. Image Process. 11(4), 467–476 (2002)
Guillaumin, M., Verbeek, J., Schmid, C.: Is that you? Metric learning approaches for face identification. In: ICCV, pp. 498–505 (2009)
Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: CVPR (1991)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)
Taigman, Y., Yang, M., Ranzato, M., Wolf, L.: DeepFace: closing the gap to human-level performance in face verification. In: CVPR, pp. 1701–1708 (2014)
Yi, D., Lei, Z., Liao, S., Li, S.Z.: Learning face representation from scratch. ar**v preprint ar**v:1411.7923 (2014)
Sun, Y., Wang, X., Tang, X.: Deep learning face representation from predicting 10,000 classes. In: CVPR, pp. 1891–1898 (2014)
Sun, Y., Wang, X., Tang, X.: Deeply learned face representations are sparse, selective, and robust. In: CVPR, pp. 2892–2900 (2015)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering. In: CVPR, pp. 815–823 (2015)
Parkhi, O.M., Vedaldi, A., Zisserman, A.: Deep face recognition. In: BMVC (2015)
Wu, X., He, R., Sun, Z.: A lightened CNN for deep face representation. ar**v preprint ar**v:1511.02683 (2015)
Wang, D., Otto, C., Jain, A.K.: Face search at scale: 80 million gallery. ar**v preprint ar**v:1507.07242 (2015)
Ding, C., Tao, D.: Robust face recognition via multimodal deep face representation. IEEE Trans. Multimedia 17(11), 2049–2058 (2015)
King, D.E.: Dlib-ml: a machine learning toolkit. J. Mach. Learn. Res. 10, 1755–1758 (2009)
Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G.: A convolutional neural network cascade for face detection. In: CVPR, pp. 5325–5334 (2015)
Goodfellow, I.J., Warde-Farley, D., Mirza, M., Courville, A., Bengio, Y.: Maxout networks. ar**v preprint ar**v:1302.4389 (2013)
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, Technical Report 07–49, University of Massachusetts, Amherst (2007)
Acknowledgements
We would like to thank the anonymous reviewers for their helpful comments. This work was supported by the STCSM’s Program (No. 16511104802).
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Shao, W., Wang, H., Zheng, Y., Ye, H. (2016). Compact Face Representation via Forward Model Selection. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_13
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DOI: https://doi.org/10.1007/978-3-319-46654-5_13
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