DeepID3: Face Recognition with Very Deep Neural Networks

Abstract

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% LFW face verification accuracy and 96.0% LFW rank-1 face identification accuracy, respectively. A further discussion of LFW face verification result is given in the end.

References in corpus (8)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Improving neural networks by preventing co-adaptation of feature detectors
• Learning Face Representation from Scratch
• Going Deeper with Convolutions
• Recover Canonical-View Faces in the Wild with Deep Neural Networks
• Surpassing Human-Level Face Verification Performance on LFW with GaussianFace
• Deeply learned face representations are sparse, selective, and robust
• Web-Scale Training for Face Identification

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