Hybrid Deep Learning for Face Verification
Publication in refereed journal


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其它資訊
摘要This 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.
著者Sun Y, Wang XG, Tang XO
期刊名稱IEEE Transactions on Pattern Analysis and Machine Intelligence,IEEE Transactions on Pattern Analysis and Machine Intelligence
出版年份2016
月份10
日期1
卷號38
期次10
出版社IEEE COMPUTER SOC
頁次1997 - 2009
國際標準期刊號0162-8828
電子國際標準期刊號1939-3539
語言英式英語
關鍵詞Convolutional networks; deep learning; face recognition
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; Engineering; Engineering, Electrical & Electronic

上次更新時間 2020-15-08 於 23:10