Abstract

This paper proposes to learn a set of high-level featurerepresentations through deep learning, referred to as Deephidden IDentity features (DeepID), for face verification.We argue that DeepID can be effectively learned throughchallenging multi-class face identification tasks, whilst theycan be generalized to other tasks (such as verification) andnew identities unseen in the training set. Moreover, thegeneralization capability of DeepID increases as more faceclasses are to be predicted at training. DeepID featuresare taken from the last hidden layer neuron activations ofdeep convolutional networks (ConvNets). When learnedas classifiers to recognize about 10, 000 face identities in the training set and configured to keep reducing the neuron numbers along the feature extraction hierarchy, these deep ConvNets gradually form compact identity-related features in the top layers with only a small number of hidden neurons. The proposed features are extracted from various face regions to form complementary and over-completerepresentations. Any state-of-the-art classifiers can belearned based on these high-level representations for face verification. 97.45% verification accuracy on LFW is achieved with only weakly aligned faces.