摘要
针对局部二值模式(Local binary pattern,LBP)及其改进算法所提取的特征维数过长、局部特征描述不够充分的缺点,提出了一种基于直方图加权HCBP(Haar-like centralized binary pattern,HCBP)的人脸表情识别方法。首先将人脸图像分成大小均匀的若干子块,利用HCBP算子提取各子块的纹理特征;然后通过信息熵的计算求得各子图像的权值,将加权子块HCBP特征直方图和原图像的HCBP直方图进行联合作为表情特征;最后,使用最近邻分类器对特征进行分类。Haar型特征与CBP相结合使得本文特征提取算法对局部特征的描述更为充分,信息熵的引入区分了人脸不同部位对表情的贡献程度。通过在JAFFE和Cohn-Kanade人脸表情库的实验证明:本文方法具有更高的识别率和识别效率。
In order to overcome the limitation of local binary pattern( LBP) and its improved algorithm,a facial expression method based on histogram weighted HCBP is proposed. Firstly,facial image is divided into some uniform sub-image,and HCBP operator is used to extract texture feature. Then the information entropy is used to calculate the weight of every sub-image,weighted HCBP histogram of sub-image is combined with the HCBP histogram of the original image,and the result histogram image is accomplished as the facial expression feature. Finally,the expression is classified with the nearest neighbor classifier. Using the combination of Haar-like feature and CBP operator makes the description of local feature more sufficient. The introduction of information entropy can distinguish the contribution of different partitions of the face. The experimental results in JAFFE library and Cohn-Kanade library show that the HCBP method outperforms than existing LBP methods in both the recognition rate and the speed.
出处
《电子测量与仪器学报》
CSCD
北大核心
2015年第7期953-960,共8页
Journal of Electronic Measurement and Instrumentation
基金
国家"863"计划(2012AA011103)
安徽省科技攻关(1206c0805039)
国家自然科学青年基金(61300119)
国家自然科学基金(61432004)资助项目
