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.
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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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