[1]白小叶[],程勇[],曹雪虹[][]. 基于光照归一化分块自适应LTP特征的人脸识别[J].计算机技术与发展,2016,26(05):56-60.
　BAI Xiao-ye[],CHENG Yong[],CAO Xue-hong[]. Face Recognition Based on Illumination Normalization and Block-based Adaptive Local Ternary Pattern[J].,2016,26(05):56-60.
点击复制

 基于光照归一化分块自适应LTP特征的人脸识别()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年05期
页码:
56-60
栏目:
智能、算法、系统工程
出版日期:
2016-05-10

文章信息/Info

Title:
 Face Recognition Based on Illumination Normalization and Block-based Adaptive Local Ternary Pattern
文章编号:
1673-629X（2016)05-0056-05
作者:
 白小叶[1] ; 程勇[2];  曹雪虹[1][2]
 1.南京邮电大学 通信与信息工程学院；2.南京工程学院 通信工程学院
Author(s):
 BAI Xiao-ye[1]; CHENG Yong[2]; CAO Xue-hong[3]
关键词:
 ; 人脸识别; 光照归一化; 自适应阈值; 局部三值模式; 分块直方图
Keywords:
 ; face recognition; illumination normalization; adaptive threshold; local ternary pattern; block histogram
分类号:
TP391
文献标志码:
A
摘要:
 针对复杂光照人脸识别的问题,文中提出一种基于光照归一化分块自适应阈值局部三值模式(Adaptive Threshold Local Ternary Pattern,ATLTP)的人脸识别算法.该方法首先对人脸图像进行光照归一化预处理,消除大部分光照影响;然后对处理后的人脸图像进行ATLTP特征提取.为了更有效地表征人脸特征,进一步将ATLTP特征矩阵划分为大小相等的子块,并对各个子块进行ATLTP特征直方图统计,最后将所有子块的直方图连接起来,构成整幅人脸图像的鉴别特征.根据最近邻准则进行分类识别,在Extended Yale B人脸库和CMU PIE人脸库上的实验结果表明,所提算法可以有效提高复杂光照人脸识别的性能.
Abstract:
 To solve the problem of face recognition under complex illumination,an effective face recognition method based on illumina-tion normalization and block-based Adaptive Threshold Local Ternary Pattern ( ATLTP) is proposed. It first performs illumination nor-malization,and eliminates most of the light effects on face images. Then ATLTP features are extracted from the processed face images. To represent the face features effectively,the feature matrix is divided into several units,and the histogram of each unit is computed and com-bined as facial features. According to the nearest neighbor principle for face recognition,the experiment on Extended Yale B face databas-es and CMU PIE face databases demonstrates that significant recognition rate can be achieved under the complex illumination conditions by the proposed method.

相似文献/References:

[1]徐钊,吴光敏,覃世欢.基于AccelDSP的LBP算法在人脸识别中的应用[J].计算机技术与发展,2014,24(01):51.
　XU Zhao,WU Guang-min,QIN Shi-huan.Application of LBP Algorithm Based on AccelDSP in Face Recognition[J].,2014,24(05):51.
[2]时书剑 马燕.基于Gabor滤波和KPCA的人脸识别方法[J].计算机技术与发展,2010,(04):51.
　SHI Shu-jian,MA Yan.Face Recognition Based on Gabor Filters and Kernel Principal Component Analysis[J].,2010,(05):51.
[3]袁健 姚明海.基于简化局部二元法的人脸特征提取[J].计算机技术与发展,2009,(06):84.
　YUAN Jian,YAO Ming-hai.Facial Feature Extraction Based on Simplified Local Binary Patterns[J].,2009,(05):84.
[4]李伟.人脸识别算法在智能手机上的实现[J].计算机技术与发展,2008,(01):161.
　LI Wei.Implementation of Face Identification in Intelligent Mobile Telephone[J].,2008,(05):161.
[5]黄国宏 刘刚.一种新的基于Fisher准则的线性特征提取方法[J].计算机技术与发展,2008,(05):227.
　HUANG Guo-hong,LIU Gang.A New Linear Feature Extraction Method Based on Fisher Criterion[J].,2008,(05):227.
[6]孙晓玲 侯德文 储凡静.人脸识别中的眼睛定位方法[J].计算机技术与发展,2008,(10):46.
　SUN Xiao-ling,HOU De-wen,CHU Fan-jing.Eye Location in Face Recogniton[J].,2008,(05):46.
[7]王静 谭同德.基于梯度和模板二次匹配的人眼定位[J].计算机技术与发展,2007,(10):144.
　WANG Jing,TAN Tong-de.A Method to Eyes Location Based on Step- Direction and Templet - Matching[J].,2007,(05):144.
[8]高宏娟 潘晨.基于非负矩阵分解的人脸识别算法的改进[J].计算机技术与发展,2007,(11):63.
　GAO Hong-juan,PAN Chen.Improved Face Recognition Algorithm Based on Non- Negative Matrix Factorization[J].,2007,(05):63.
[9]徐勇 张海 周森鑫 王辉.基于统计学习理论的人脸识别方法研究[J].计算机技术与发展,2007,(11):118.
　XU Yong,ZHANG Hai,ZHOU Sen-xin,et al.Research on Face Recognition Based on Statistical Learning Theory[J].,2007,(05):118.
[10]马驰 阮秋琦.基于离散微粒群优化算法的SVM参数选择[J].计算机技术与发展,2007,(12):20.
　MA Chi,RUAN Qiu-qi.Parameter Selection for SVM Based on Discrete PSO[J].,2007,(05):20.
[11]王园萍,殷洪友. 基于矩阵分数范数的人脸识别方法[J].计算机技术与发展,2015,25(04):22.
　WANG Yuan-ping,YIN Hong-you. Face Recognition Method Based on Fractional Matrix Norm[J].,2015,25(05):22.
[12]石正权,赵启军,陈虎. 基于CPR和CLM的多视角人脸特征点定位方法[J].计算机技术与发展,2015,25(11):1.
　SHI Zheng-quan,ZHAO Qi-jun,CHEN Hu. A Multi-view Facial Landmark Localization Method Based on CPR and CLM[J].,2015,25(05):1.
[13]谢文浩,翟素兰. 基于加权稀疏近邻表示的人脸识别[J].计算机技术与发展,2016,26(02):22.
　XIE Wen-hao,ZHAI Su-lan. Face Recognition Based on Weighted Sparse Neighbor Representation[J].,2016,26(05):22.
[14]吴锡生,魏月纳. 基于特征融合的人脸识别新算法[J].计算机技术与发展,2017,27(01):65.
　WU Xi-sheng,WEI Yue-na. A New Face Recognition Algorithm Based on Feature Fusion[J].,2017,27(05):65.
[15]谢尚高,王丽平. 基于同类测试样本组的稀疏表示人脸识别[J].计算机技术与发展,2017,27(08):7.
　XIE Shang-gao,WANG Li-ping. Sparse Representation Classification for Face Recognition with Intra-class Testing-sample Group[J].,2017,27(05):7.
[16]翟社平,李炀,马蒙雨,等. 基于LBP和SVM的人脸检测[J].计算机技术与发展,2017,27(09):44.
　ZHAI She-ping,LI Yang,MA Meng-yu,et al. Face Detection Based on LBP and SVM[J].,2017,27(05):44.

更新日期/Last Update: 2016-09-19