swMATH ID: 39588
Software Authors: Yi Sun, Ding Liang, Xiaogang Wang, Xiaoou Tang
Description: DeepID3: Face Recognition with Very Deep Neural Networks. The state-of-the-art of face recognition has been significantly advanced by the emergence of deep learning. Very deep neural networks recently achieved great success on general object recognition because of their superb learning capacity. This motivates us to investigate their effectiveness on face recognition. This paper proposes two very deep neural network architectures, referred to as DeepID3, for face recognition. These two architectures are rebuilt from stacked convolution and inception layers proposed in VGG net and GoogLeNet to make them suitable to face recognition. Joint face identification-verification supervisory signals are added to both intermediate and final feature extraction layers during training. An ensemble of the proposed two architectures achieves 99.53
Homepage: https://paperswithcode.com/paper/deepid3-face-recognition-with-very-deep
Related Software: DeepFace; FaceNet; ImageNet; ShuffleNet; AdaCos; SphereFace; LFW; Inception-v4; Xception; AlexNet; Adam; Caffe; SIFT; MagFace; EfficientNet; CosFace; FaceX-Zoo; AttentionNet; CPLFW; WebFace260M
Cited in: 3 Publications

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