Abstract
Face recognition plays a critical role in surveillance and security systems. Due to the large appearance variation of human faces, the dissimilarity among faces for the same person may be quite large. This leads to unstable results. To improve the stability and reliability of face recognition, this paper proposes a novel deep-based approach by introducing an adaptively-weighted verification loss function. The proposed loss function can properly enlarge the margin between positive face pairs and negative face pairs from the global perspective, thus obtain a more reliable recognition model by minimizing the dissimilarity between same-person faces and maximizing the dissimilarity between different-person faces. Experiments on the benchmark LFW and YTF datasets demonstrate that the proposed approach can obtain the state-of-the-art performances for face recognition.
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Acknowledgement
This work is supported by Tencent Research Grant, National Science Foundation of China (61471235, 61720106001), and Shanghai “The Belt and Road” Young Scholar Exchange Grant (17510740100).
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Qiu, F., Lin, W., Liu, X., Yu, H., Xiong, H. (2018). Deep Face Recognition Using Adaptively-Weighted Verification Loss Function. In: Zhai, G., Zhou, J., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2017. Communications in Computer and Information Science, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-10-8108-8_17
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