一亿ID的人脸识别训练和万亿人脸对(Trillion Pairs)的人脸识别评测-5

程序员研修院
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2023-01-12 15:17:25
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One of the main challenges in feature learning using Deep Convolutional Neural Networks (DCNNs) for large- scale face recognition is the design of appropriate loss func- tions that enhance discriminative power. Centre loss pe- nalises the distance between the deep features and their cor- responding class centres in the Euclidean space to achieve intra-class compactness. SphereFace assumes that the lin- ear transformation matrix in the last fully connected layer can be used as a representation of the class centres in an angular space and penalises the angles between the deep features and their corresponding weights in a multiplicative way. Recently, a popular line of research is to incorporate margins in well-established loss functions in order to max- imise face class separability. In this paper, we propose an Additive Angular Margin Loss (ArcFace) to obtain highly discriminative features for face recognition. The proposed ArcFace has a clear geometric interpretation due to the ex- act correspondence to the geodesic distance on the hyper- sphere. We present arguably the most extensive experimen- tal evaluation of all the recent state-of-the-art face recog- nition methods on over 10 face recognition benchmarks in- cluding a new large-scale image database with trillion level of pairs and a large-scale video dataset. We show that Ar- cFace consistently outperforms the state-of-the-art and can be easily implemented with negligible computational over- head. We release all refined training data, training codes, pre-trained models and training logs 1 , which will help re- produce the results in this paper

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