摘要
为了避免传统表情识别方法中复杂的特征手动提取过程,同时能够提取到更多的表情特征,本文提出一种双路特征融合模型,将卷积神经网络(CNN)和方向梯度直方图(HOG)方法结合起来进行研究.在第一条通道上,对人脸表情图像进行归一化预处理,并使用可训练的卷积核提取隐式特征;在第二条通道上,提取出人脸面部表情的HOG特征,然后输入到卷积神经网络中的全连接层上;最后将融合特征传递至输出层,采用Softmax分类器进行识别并输出结果.本文在FER2013和CK+表情数据库上进行实验,结果验证了方法的有效性.
In order to avoid the complex manual feature extraction process in traditional facial expression recognition methods and to extract more facial features,we propose a Dual-Path Feature Fusion model that combines Convolutional Neural Network(CNN)with Histogram of Oriented Gradient(HOG).In the first channel,the facial expression image is normalized,and the trainable convolution kernel is used to extract the implicit features.In the second channel,the HOG of facial expression are extracted and transmitted to the full connection layer of CNN.Finally,the fusion features are transferred to the output layer,and the recognition results are obtained via the Softmax classifier.This paper conducted an experiment on FER2013&CK+database,the results verify that the method presented is effective.
作者
丁名都
李琳
DING Mingdu;LI Lin(College of Optoelectronic Information and Computer Engineering,Shanghai University of Science and Technology,Shanghai 200093,China)
出处
《信息与控制》
CSCD
北大核心
2020年第1期47-54,共8页
Information and Control
基金
国家自然科学基金资助项目(61673277).
关键词
表情识别
双路特征融合
卷积神经网络
方向梯度直方图
expression recognition
dual-path feature fusion
convolutional neural network
histogram of oriented gradient
