Abstract
The performance of feature learning for deep convolutional neural networks (DCNNs) is increasing promptly with significant improvement in numerous applications. Recent studies on loss functions clearly describing that better normalization is helpful for improving the performance of face recognition (FR). Several methods based on different loss functions have been proposed for FR to obtain discriminative features. In this paper, we propose an additive parameter depending on multiplicative angular margin to improve the discriminative power of feature embedding that can be easily implemented. In additive parameter approach, an automatic adjustment of the seedling element as the result of angular marginal seed is offered in a particular way for the angular softmax to learn angularly discriminative features. We train the model on publically available dataset CASIA-WebFace, and our experiments on famous benchmarks YouTube Faces (YTF) and labeled face in the wild (LFW) achieve better performance than the various state-of-the-art approaches.
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Acknowledgements
1J.U. Rahman, supported by CAS-TWAS President’s Fellowship at University of Science and Technology of China, No. 96, JinZhai Road Baohe District, Hefei, Anhui, 230026, People’s Republic of China. We would like to thank the anonymous reviewers for their comments and suggestions which greatly improve the manuscript. The work is supported by the NSF of China (No. 11871447), and Anhui Initiative in Quantum Information Technologies (AHY150200).
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Ul Rahman, J., Chen, Q. & Yang, Z. Additive Parameter for Deep Face Recognition. Commun. Math. Stat. 8, 203–217 (2020). https://doi.org/10.1007/s40304-019-00198-z
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DOI: https://doi.org/10.1007/s40304-019-00198-z