基于多特征融合卷积神经网络的人脸表情识别

针对卷积神经网络特征提取不够充分且识别率低等问题,提出了一种多特征融合卷积神经网络的人脸表情识别方法。首先,为了增加网络的宽度和深度,在网络中引入Inception结构来提取特征的多样性;然后,将提取到的高层次特征与低层次特征进行融合,利用池化层的特征,将融合后的特征送入全连接层,对其特征进行融合处理来增加网络的非线性表达,使网络学习到的特征更加丰富;最后,输出层经过Softmax分类器对表情进行分类,在公开数据集FER2013和CK+上进行实验,并且对实验结果进行分析。实验结果表明:改进后的网络结构在FER2013和CK+数据集的面部表情上,识别率分别提高了0.06%和2.25%。所提方法在人脸表情识别中对卷积神经网络设置和参数配置方面具有参考价值。

Facial expression recognition based on multi-feature fusion convolution network

Aiming at the problem of insufficient feature extraction and low recognition rate of convolutional neural network, a novel facial expression recognition method based on multi-feature fusion convolutional neural network is proposed. First, to increase the width and depth of the network, Inception architecture is introduced into the network to extract the diversity of features; Then, the extracted high-level features are fused with the low-level features, and the pooled features are used to send the fused features into the full connection layer, then the fused features are processed to increase the non-linear expression of the network and enrich the features learned by the network. Finally, the output layer classifies the expressions by Softmax classifier, conductes experiments on FER2013 and CK+, and analyzes the experimental results. Experimental results show that the improved network structure improves the recognition rate of facial expressions in FER2013 and CK+ data sets by 006% and 225%, respectively. The proposed method is valuable for setting up convolution neural network and parameter configuration in facial expression recognition.