A novel face recognition algorithm based on the combination of LBP and CNN

• Main Authors: Ke, Pengfei, Cai, Maoguo, Wang, Hanmo, Chen, Jialong
• Format: Conference Proceeding
• Language: English
• Subjects: Convolution; Convolutional Neural Network; Convolutional neural networks; Face; Face recognition; Feature extraction; Local Binary Pattern; Training
• ISSN: 2164-5221
• DOI: 10.1109/ICSP.2018.8652477

In order to overcome the effects of posture, illumination, expression and other factors on face recognition, this paper proposes an algorithm which is based on local binary pattern (LBP) and convolutional neural network (CNN). LBP is a texture description method which describes the local texture features of an image. It has good robustness of illumination and posture. CNN can effectively extract the spatial features of images and reduce the dimensions of features. This paper combines the advantages of LBP and CNN to improve the accuracy of face recognition. The CNN in this algorithm has four convolution layers, two max-pooling layers, one activation layer, one fully connected layer, and one output layer. In order to optimize the network structure, batch normalization layer is added after the convolution layer. We get the local binary pattern coded images and put the images as the input of the CNN and train the network. Hence, we can use the well-trained CNN for classification and identification. The Experiments on the CMU-PIE face database show that our algorithm can effectively improve the rate of face recognition.