Laser & Optoelectronics Progress, Volume. 57, Issue 4, 041513(2020)
Facial Expression Recognition Based on Local Feature Fusion of Convolutional Neural Network
Herein, a facial expression recognition method based on local feature fusion of convolutional neural network (CNN) is proposed to improve recognition rate and real-time performance of facial expression classification. First, a CNN model is constructed to learn the local features of the eyes, eyebrows, and mouth. Then, the local features are sent to a support vector machine multi-classifier to obtain their posterior probabilities. Finally, a particle swarm optimization algorithm is used to optimize the fusion weight of each feature, realize the decision-level fusion with the optimal accuracy rate, and complete the expression classification. Experiments show that the average recognition rates of the method on the CK+ and JAFFE databases are 94.56% and 97.08%, respectively. Compared with other recognition methods, results show that the proposed method has superior performance, improves the recognition rate and robustness, and ensures the real-time performance of the classification.
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Lisha Yao, Guoming Xu, Feng Zhao. Facial Expression Recognition Based on Local Feature Fusion of Convolutional Neural Network[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041513
Category: Machine Vision
Received: Mar. 15, 2019
Accepted: Apr. 30, 2019
Published Online: Feb. 20, 2020
The Author Email: Xu Guoming (313910355@qq.com)