Face Recognition Based on Deep Aggregated Sparse Autoencoder Network

Sparse autoencoder network is sensitive to face noise, and the learning process is easy to ignore the face structure information. Address this problem, we propose a face recognition approach fused sub-region LBP feature and deep aggregated sparse autoencoder network. Firstly, the face image is divided into different sub-regions, and the local binary pattern is used to pre-process the face image in order to obtain the LBP features of different sub-region faces. Then, the deep features for different sub-region LBP features are extracted with different sparse autoencoders. Finally, the output features of different sparse autoencoders are aggregated by full connection, and the face feature vectors are obtained for classification. We get the optimal aggregated network structure and network parameters through lots of experiments, while the face recognition accuracy is also improved.