激光与光电子学进展, 2020, 57 (4): 041513, 网络出版: 2020-02-20
基于卷积神经网络局部特征融合的人脸表情识别 下载: 1483次
Facial Expression Recognition Based on Local Feature Fusion of Convolutional Neural Network
机器视觉 表情识别 卷积神经网络 决策融合 machine vision expression recognition convolutional neural network decision fusion
摘要
为提高人脸表情分类的识别率和实时性,提出一种基于卷积神经网络(CNN)局部特征融合的人脸表情识别方法。首先,构建CNN模型,学习眼睛、眉毛、嘴巴3个局部区域的局部特征;然后,将局部特征送入到支持向量机(SVM)多分类器中获取各类特征的后验概率;最后,通过粒子群寻优算法优化各特征的最优融合权值,实现正确率最优的决策级融合,完成表情分类。实验表明,本文方法在CK+和JAFFE数据库的平均识别率分别达到了94.56%和97.08%,与其他识别方法相比,本文方法性能优越,能提高算法的识别率和稳健性,同时保证了算法的实时性。
Abstract
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.
姚丽莎, 徐国明, 赵凤. 基于卷积神经网络局部特征融合的人脸表情识别[J]. 激光与光电子学进展, 2020, 57(4): 041513. 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.