Abstract
Face recognition has caught a lot of attention and plenty of valuable methods have been proposed during the past decades. However, because it is hard to learn geometrically invariant representations, existing face recognition methods still perform relatively poorly in conducting multi-angle face recognition. In this paper, we hypothesize that there is an inherent mapping between the frontal and non-frontal faces, and the non-frontal face representations can be converted into the frontal face representations by an equivariant mapping. To carry out the mapping, we propose a Multi-Angle Deep Residual Equivariant Mapping (MADREM) block which adaptively maps the non-frontal face representation to the frontal face representation. It can be considered the MADREM block carry out face alignment and face normalization in the feature space. The residual equivariant mapping block can enhance the discriminative power of the face representations. Finally, we achieve an accuracy of 99.78% on the LFW dataset and 94.25% on CFP-FP dataset based on proposed multiscale-convolution and residual equivariant mapping block.
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Acknowledgement
This work was funded by Economic, Trade and information Commission of Shenzhen Municipality (20170504160426188) and National Natural Science Foundation of China (U1836205).
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Liu, W., Wu, L., Xu, Y., Wang, D. (2019). Deep Residual Equivariant Mapping for Multi-angle Face Recognition. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_16
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