关键词: 人脸识别, 局部二值模式特征, 深度学习, 深度信念网络, 特征提取

Abstract:

In order to solve the problem that deep learning ignores the local structure features of faces when it extracts face feature in face recognition, a novel face recognition approach which combines block Local Binary Pattern (LBP) and deep learning was presented. At first, LBP features were extracted from different blocks of a face image, which were connected together to serve as the texture description for the whole face. Then, the LBP feature was input to a Deep Belif Network (DBN), which was trained level by level to obtain classification capability. At last, the trained DBN was used to recognize unseen face samples. On ORL, YALE and FERET face databases, the experimental results show that the proposed method has a better recognition performance compared with Support Vector Machine (SVM) in small sample face recognition.

Key words: face recognition, Local Binary Pattern (LBP) feature, deep learning, deep belief network, feature extraction