Face recognition under unconstrained based on LBP and deep learning - Shu-fen LIANG; Yin-hua LIU; Li-chen LI (Tongxin xuebao)
A face recognition method under unconstrained condition was proposed based on deep learning. At the same time, making LBP texture features as the input of deep learning net, and greedy training the network layer was made by layer to obtain good network parameters. At last, the trained net was used to predict the test samples' labels. The results of experiments on LFW(labeled faces in the wild) show that the algorithm can obtain higher recognition rate than traditional algorithms(such as PCA, SVM, LBP).Otherwise, the recognition rate on Yale and Yale-B are also very high, the experi-mental results show that deep learning net with LBP texture as its input can classify face images correctly.