FaceX-Zoo | Proceedings of the 29th ACM International Conference on Multimedia (2024)

1. 2018. https://github.com/deepinsight/insightface.Google Scholar
2. 2020. https://github.com/ZhaoJ9014/face.evoLVe.PyTorch.Google Scholar
3. Brandon Amos, Bartosz Ludwiczuk, and Mahadev Satyanarayanan. 2016. Open- Face: A general-purpose face recognition library with mobile applications. Technical Report.Google Scholar
4. Sheng Chen, Yang Liu, Xiang Gao, and Zhen Han. 2018. Mobilefacenets: Efficient cnns for accurate real-time face verification on mobile devices. In CCBR.Google Scholar
5. Jiankang Deng, Jia Guo, Niannan Xue, and Stefanos Zafeiriou. 2019. Arcface: Additive angular margin loss for deep face recognition. In CVPR.Google Scholar
6. Hang Du, Hailin Shi, Yuchi Liu, Jun Wang, Zhen Lei, Dan Zeng, and Tao Mei. 2020. Semi-Siamese Training for Shallow Face Learning. In ECCV.Google Scholar
7. Yao Feng, Fan Wu, Xiaohu Shao, Yanfeng Wang, and Xi Zhou. 2018. Joint 3d face reconstruction and dense alignment with position map regression network. In ECCV.Google Scholar
8. Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In CVPR.Google Scholar
9. Yibo Hu, Xiang Wu, and Ran He. 2020. TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search. In ECCV.Google Scholar
10. Gary B Huang, Marwan Mattar, Tamara Berg, and Eric Learned-Miller. 2008. Labeled faces in the wild: A database forstudying face recognition in unconstrained environments. In Workshop on Faces in 'Real-Life' Images: Detection, Alignment, and Recognition.Google Scholar
11. Ira Kemelmacher-Shlizerman, Steven M Seitz, Daniel Miller, and Evan Brossard. 2016. The megaface benchmark: 1 million faces for recognition at scale. In CVPR.Google Scholar
12. Yinglu Liu, Hailin Shi, Hao Shen, Yue Si, Xiaobo Wang, and Tao Mei. 2020. A New Dataset and Boundary-Attention Semantic Segmentation for Face Parsing. In AAAI.Google Scholar
13. Qiang Meng, Shichao Zhao, Zhida Huang, and Feng Zhou. 2021. Magface: A universal representation for face recognition and quality assessment. In CVPR.Google Scholar
14. Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. In NIPS. Google ScholarDigital Library
15. Yifan Sun, Changmao Cheng, Yuhan Zhang, Chi Zhang, Liang Zheng, Zhongdao Wang, and Yichen Wei. 2020. Circle loss: A unified perspective of pair similarity optimization. In CVPR.Google Scholar
16. Mingxing Tan and Quoc Le. 2019. Efficientnet: Rethinking model scaling for convolutional neural networks. In ICML.Google Scholar
17. Feng Wang, Jian Cheng, Weiyang Liu, and Haijun Liu. 2018. Additive margin softmax for face verification. ISPL (2018).Google Scholar
18. Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, and Xiaoou Tang. 2017. Residual attention network for image classification. In CVPR.Google Scholar
19. Xiaobo Wang, Shifeng Zhang, Shuo Wang, Tianyu Fu, Hailin Shi, and Tao Mei. 2020. Mis-classified vector guided softmax loss for face recognition. In AAAI.Google Scholar

Index Terms

1. FaceX-Zoo: A PyTorch Toolbox for Face Recognition

   1. Computing methodologies

      1. Artificial intelligence

         1. Computer vision

            1. Computer vision representations

               1. Image representations

            2. Computer vision tasks

               1. Biometrics

   2. Software and its engineering

      1. Software notations and tools

         1. Software libraries and repositories

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  Abstract

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