Deep Learning Face Representation from Predicting 10,000 Classes
Top Cited Papers
- 1 June 2014
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1891-1898
- https://doi.org/10.1109/cvpr.2014.244
Abstract
This paper proposes to learn a set of high-level feature representations through deep learning, referred to as Deep hidden IDentity features (DeepID), for face verification. We argue that DeepID can be effectively learned through challenging multi-class face identification tasks, whilst they can be generalized to other tasks (such as verification) and new identities unseen in the training set. Moreover, the generalization capability of DeepID increases as more face classes are to be predicted at training. DeepID features are taken from the last hidden layer neuron activations of deep convolutional networks (ConvNets). When learned as 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-complete representations. Any state-of-the-art classifiers can be learned based on these high-level representations for face verification. 97:45% verification accuracy on LFW is achieved with only weakly aligned faces.Keywords
This publication has 25 references indexed in Scilit:
- Deep Learning Identity-Preserving Face SpacePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2013
- Learning Hierarchical Features for Scene LabelingIEEE Transactions on Pattern Analysis and Machine Intelligence, 2012
- Learning hierarchical representations for face verification with convolutional deep belief networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Multi-column deep neural networks for image classificationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2012
- Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysisPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- An associate-predict model for face recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2011
- Is that you? Metric learning approaches for face identificationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Attribute and simile classifiers for face verificationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2009
- Pedestrian Detection via Classification on Riemannian ManifoldsIEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
- Information-theoretic metric learningPublished by Association for Computing Machinery (ACM) ,2007