Jialin Tang1, Qinglang Su2, Binghua Su3, Simon Fong4, Wei Cao5, Xueyuan Gong6. 1. Beijing Institute of Technology, Zhuhai 519088, China; City University of Macau, Macau, China. Electronic address: thong03@qq.com. 2. City University of Macau, Macau, China. Electronic address: sonnysu@cityu.mo. 3. Beijing Institute of Technology, Zhuhai 519088, China. Electronic address: 01004@bitzh.edu.cn. 4. Department of Computer and Information Science, University of Macau, Macau, China. Electronic address: ccfong@um.edu.mo. 5. Beijing Institute of Technology, Zhuhai 519088, China. Electronic address: 17303@bitzh.edu.cn. 6. Beijing Institute of Technology, Zhuhai 519088, China. Electronic address: 18202@bitzh.edu.cn.
Abstract
BACKGROUND AND OBJECTIVE: Face recognition success rate is influenced by illumination, expression, posture change, and other factors, which is due to the low generalization ability of a single convolutional neural network. A new face recognition method based on parallel ensemble learning of convolutional neural networks (CNN) and local binary patterns (LBP) is proposed to solve this problem. It also helps to improve the low pedestrian detection rate caused by occlusion. METHODS: First, the LBP operator is employed to extract features of the face texture. After that, 10 convolutional neural networks with 5 different network structures are adopted to further extract features for training, to improve the network parameters and get classification result by using the Softmax function after the layer is fully connected. Finally, the method of parallel ensemble learning is used to generate the final result of face recognition using majority voting. RESULTS: By this method, the recognition rates in the ORL and Yale-B face datasets increase to 100% and 97.51%, respectively. In the experiments, the proposed approach is illustrated not only enhances its tolerance to illumination, expression, and posture but also improves the accuracy of face recognition and the poor generalization performance of the model, which is normally caused by the learning algorithm being trapped in a local minimum. Moreover, the proposed method is combined with a pedestrian detection model as a hybrid model for improving the detection rate, which shows in the result that the detection rate is improved by 11.2%. CONCLUSION: In summary, the proposed approach greatly outperforms other competitive methods.
BACKGROUND AND OBJECTIVE: Face recognition success rate is influenced by illumination, expression, posture change, and other factors, which is due to the low generalization ability of a single convolutional neural network. A new face recognition method based on parallel ensemble learning of convolutional neural networks (CNN) and local binary patterns (LBP) is proposed to solve this problem. It also helps to improve the low pedestrian detection rate caused by occlusion. METHODS: First, the LBP operator is employed to extract features of the face texture. After that, 10 convolutional neural networks with 5 different network structures are adopted to further extract features for training, to improve the network parameters and get classification result by using the Softmax function after the layer is fully connected. Finally, the method of parallel ensemble learning is used to generate the final result of face recognition using majority voting. RESULTS: By this method, the recognition rates in the ORL and Yale-B face datasets increase to 100% and 97.51%, respectively. In the experiments, the proposed approach is illustrated not only enhances its tolerance to illumination, expression, and posture but also improves the accuracy of face recognition and the poor generalization performance of the model, which is normally caused by the learning algorithm being trapped in a local minimum. Moreover, the proposed method is combined with a pedestrian detection model as a hybrid model for improving the detection rate, which shows in the result that the detection rate is improved by 11.2%. CONCLUSION: In summary, the proposed approach greatly outperforms other competitive methods.