结合LBP特征和深度学习的人脸表情识别  

Facial Expression Recognition Based on LBP Features and DeepLearning

作　　者：张广世 葛广英[1] 朱荣华 孙群 Zhang Guangshi;Ge Guangying;Zhu Ronghua;Sun Qun(College of Physics and Information Engineering,Liaocheng University,Liaocheng 252059,China;College of Mechanical and Automotive Engineering,Liaocheng University,Liaocheng 252059,China)

机构地区：[1]聊城大学物理科学与信息工程学院,山东聊城252059 [2]聊城大学机械与汽车工程学院,山东聊城252059

出　　处：《计算机测量与控制》2020年第2期174-178,共5页Computer Measurement &Control

基　　金：中央引导地方科技发展专项资金计划

摘　　要：对于人脸表情识别,传统方法是先提取图像特征,再使用机器学习方法进行识别,这种方法不但特征提取过程复杂且泛化能力也差;为了达到更好的人脸表情识别效果,文中提出一种结合特征提取和卷积神经网络的人脸表情识别方法;首先使用基于Haar-like特征的AdaBoost算法对于数据库原始图片进行人脸区域检测,然后提取人脸区域局部二值模式(Local Binary Patterns,LBP)特征图,将其尺寸归一化后输入到改进的LeNet-5神经网络模型中进行识别;在CK+和JAFFE数据集上采用10折交叉验证方法进行实验,分别为98.19%和96.35%的准确率;实验结果表明该方法与其他主流方法相比在人脸表情识别上有一定的先进性和有效性。For facial expressions recognition,the traditional method is to execute feature extraction and recognize by machine learning.This method not only has complex feature extraction process but also poor generalization.In order to achieve better facial expression recognition,the paper proposes a facial expression recognition method combining feature extraction and convolutional neural network.Firstly,the AdaBoost algorithm based on Haar-like feature is used to detect the face region of the original image of the database,and then extract the local binary patterns(LBP)feature map of the face region,normalize the size and input it into the improved LeNet-5 network to recognize.The recognition rate is 98.19%and 96.35%respectively in the CK+and JAFFE database with 10-fold Cross-validation method.The experimental results show that this method has certain advancement and effectiveness in facial expression recognition compared with other mainstream methods.

关 键 词：图像处理 LBP特征 人脸检测 卷积神经网络 人脸表情识别 

分 类 号：TN919.8[电子电信—通信与信息系统]