摘要
针对单样本环境下传统人脸识别算法识别效果不佳的问题,提出一种结合改进中心对称局部二值模式和位平面分解的单样本人脸识别算法(ICSDBP)。采用改进中心对称局部二值模式算子提取人脸的特征信息得到两幅不同半径的纹理特征图像,将每幅纹理特征图像分解为4幅位平面图像,最后将8幅特征图像串联融合,使用最近邻分类器进行分类识别。在AR、CAS-PEAL和Extend Yale B人脸数据库上的仿真结果表明,该算法具有较高的识别率和较快的识别速度,对光照和表情等变化具有较好的稳健性。
To overcome the problem of poor recognition of traditional face recognition algorithms in single sample environment,a single sample face recognition algorithm combining improved center-symmetric local binary pattern and bit-plane decomposition（ICSDBP）is proposed.Firstly,the texture feature of a face image is extracted by the improved center-symmetric local binary pattern operator to obtain two texture feature images with different radii,and then each texture feature image is decomposed into 4 bit-plane images.Finally,eight feature images are combined in series,and the nearest neighbor classifier is used for classification and recognition.The simulation results on the AR,CAS-PEAL and Extend Yale B face databases show that the proposed algorithm has high recognition rate and high recognition speed,and it is robust to the variations of face illumination and face expression.
作者
杨恢先
张翡
陈永
刘建
周彤彤
Yang Huixian;Zhang Fei;Chen Yong;Liu Jian;Zhou Tongtong(Physics and Optoelectronic Engineering College,Xiangtan University,Xiangtan,Hunan 411105,China;Mechanical and Electrical Engineering College,Hunan Institute of Applied Technology,Changde,Hunan 415000,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2018年第7期215-222,共8页
Laser & Optoelectronics Progress
基金
湖南省教育厅科学研究项目(15C1009)
关键词
图像处理
人脸识别
单样本
中心对称局部二值模式
位平面分解
最近邻分类器
image processing
face recognition
single sample
center-symmetric local binary pattern
bit-plane decomposition
nearest neighbor classifier
