Hybrid Deep Learning for Face Verification
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AbstractThis paper proposes a hybrid convolutional network (ConvNet)-Restricted Boltzmann Machine (RBM) model for face verification. A key contribution of this work is to learn high-level relational visual features with rich identity similarity information. The deep ConvNets in our model start by extracting local relational visual features from two face images in comparison, which are further processed through multiple layers to extract high-level and global relational features. To keep enough discriminative information, we use the last hidden layer neuron activations of the ConvNet as features for face verification instead of those of the output layer. To characterize face similarities from different aspects, we concatenate the features extracted from different face region pairs by different deep ConvNets. The resulting high-dimensional relational features are classified by an RBM for face verification. After pre-training each ConvNet and the RBM separately, the entire hybrid network is jointly optimized to further improve the accuracy. Various aspects of the ConvNet structures, relational features, and face verification classifiers are investigated. Our model achieves the state-of-the-art face verification performance on the challenging LFW dataset under both the unrestricted protocol and the setting when outside data is allowed to be used for training.
All Author(s) ListSun Y, Wang XG, Tang XO
Journal nameIEEE Transactions on Pattern Analysis and Machine Intelligence,IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume Number38
Issue Number10
Pages1997 - 2009
LanguagesEnglish-United Kingdom
KeywordsConvolutional networks; deep learning; face recognition
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Engineering; Engineering, Electrical & Electronic

Last updated on 2021-17-09 at 01:34