加权信息熵与增强局部二值模式结合的人脸识别

doi:10.11772/j.issn.1001-9081.2019010181

摘要
图/表
参考文献(0)
相关文章 (15)

全文: PDF (1131 KB) HTML
输出: BibTeX | EndNote (RIS)

摘要针对人脸识别因光照、姿态、表情、遮挡及噪声等多种因素的影响而导致的识别率不高的问题，提出一种加权信息熵（IEw）与自适应阈值环形局部二值模式（ATRLBP）算子相结合的人脸识别方法（IE （w） ATR-LBP）。首先，从原始人脸图像分块提取信息熵，得到每个子块的IEw；然后，利用ATRLBP算子分别对每个人脸子块提取特征从而得到概率直方图；最后，将各个块的IEw与概率直方图相乘，再串联成为原始人脸图像最后的特征直方图，并利用支持向量机（SVM）对人脸进行识别。在AR人脸库的表情、光照、遮挡A和遮挡B四个数据集上，IE （w） ATR-LBP方法分别取得了98.37%、94.17%、98.20%和99.34%的识别率。在ORL人脸库上，IE （w） ATR-LBP方法的最大识别率为99.85%；而且在ORL人脸库5次不同训练样本的实验中，与无噪声时相比，加入高斯和椒盐噪声后的平均识别率分别下降了14.04和2.95个百分点。实验结果表明，IE （w） ATR-LBP方法能够有效提高人脸在受光照、姿态、遮挡等影响时的识别率，尤其是存在表情变化及脉冲类噪声干扰时的识别率。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	丁莲静
	刘光帅
	李旭瑞
	陈晓文

关键词 ：人脸识别, 局部二值模式, 加权信息熵, 自适应阈值, 深度学习

Abstract：Under the influence of illumination, pose, expression, occlusion and noise, the recognition rate of faces is excessively low, therefore a method combining weighted Information Entropy (IEw) with Adaptive-Threshold Ring Local Binary Pattern (ATRLBP) (IEwATR-LBP) was proposed. Firstly, the information entropy was extracted from the sub-blocks of the original face image, and then the IEw of each sub-block was obtained. Secondly, the probability histogram was obtained by using ATRLBP operator to extract the features of face sub-blocks. Finally, the final feature histogram of original face image was obtained by concatenating the multiplications of each IEw with the probability histogram, and the recognition result was calculated through Support Vector Machine (SVM). In the comparison experiments on the illumination, pose, expression and occlusion datasets from AR face database, the proposed method achieved recognition rates of 98.37%, 94.17%, 98.20%, and 99.34% respectively; meanwile, it also achieved the maximum recognition rate of 99.85% on ORL face database. And the average recognition rates in 5 experiments with different training samples were compared to conclude that the recognition rate of samples with Gauss noise was 14.04 percentage points lower than that of samples without noise, while the recognition rate of samples with salt & pepper noise was only 2.95 percentage points lower than that of samples without noise. Experimental results show that the proposed method can effectively improve the recognition rate of faces under the influence of illumination, pose, occlusion, expression and impulse noise.

Key words： face recognition Local Binary Pattern (LBP) weighted information entropy adaptive threshold deep learning

收稿日期: 2019-01-25 出版日期: 2019-04-17

中图分类号:

TP391.4

基金资助:国家自然科学基金资助项目（51275431）；四川省科技支撑计划项目（2015GZ0200）。

通讯作者: 刘光帅 E-mail: motorlgs@163.com

作者简介: 丁莲静(1992-),男,贵州凤岗人,硕士研究生,主要研究方向:图像识别、机器视觉;刘光帅(1978-),男,贵州天柱人,副教授,博士,主要研究方向:逆向工程、图形图像处理;李旭瑞(1996-),男,山西曲沃人,硕士研究生,主要研究方向:模式识别、机器视觉;陈晓文(1995-),男,湖北应城人,硕士研究生,主要研究方向:模式识别、机器视觉。

引用本文:

丁莲静, 刘光帅, 李旭瑞, 陈晓文. 加权信息熵与增强局部二值模式结合的人脸识别[J]. 计算机应用, 2019, 39(8): 2210-2216. DING Lianjing, LIU Guangshuai, LI Xurui, CHEN Xiaowen. Face recognition combining weighted information entropy with enhanced local binary pattern. Journal of Computer Applications, 2019, 39(8): 2210-2216.

链接本文:

http://www.joca.cn/CN/10.11772/j.issn.1001-9081.2019010181 或 http://www.joca.cn/CN/Y2019/V39/I8/2210

[1] TURK M, PENTLAND A. Eigenfaces for recognition[J]. Journal of Cognitive Neuroscience,1991,3(1):71-86.
[2] CHELLAPPA R, WILSON C L, SIROHEY S. Human and machine recognition of faces:a survey[J]. Proceedings of the IEEE, 1995, 83(5):705-741.
[3] BELHUMEUR P N, HESPANHA J P, KRIEGMAN D J. Eigenfaces vs. Fisherfaces:recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7):711-720.
[4] 彭中亚,程国建.基于独立成分分析和核向量机的人脸识别[J].计算机工程,2010,36(7):193-194. (PENG Z Y, CHENG G J. Face recognition based on independent component analysis and core vector machines[J]. Computer Engineering, 2010, 36(7):193-194.)
[5] 胡敏,余子玺,王晓华,等.G-LBP和方差投影交叉熵的人脸识别[J].图学学报,2017,38(1):82-89. (HU M, YU Z X, WANG X H, et al. G-LBP and variance cross projection function for face recognition[J]. Journal of Graphics, 2017, 38(1):82-89.)
[6] 梁淑芬,刘银华,李立琛.基于LBP和深度学习的非限制条件下人脸识别算法[J].通信学报,2014,35(6):154-160. (LIANG S F, LIU Y H, LI L C. Face recognition under unconstrained based on LBP and deep learning[J]. Journal on Communications, 2014, 35(6):154-160.)
[7] LECUN Y, BOSER B, DENKER J S, et al. Backpropagation applied to handwritten zip code recognition[J]. Neural Computation, 1989, 1(4):541-551.
[8] SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2015:1-9.
[9] HE K, HANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2016:770-778.
[10] 杨瑞, 张云伟, 苟爽, 等. Gabor特征与深度信念网络结合的人脸识别方法[J]. 传感器与微系统, 2017, 36(5):68-70. (YANG R, ZHANG Y W, GOU S, et al. Face recognition algorithm based on Gabor feature and DBN[J]. Transducer and Microsystem Technologies, 2017, 36(5):68-70.)
[11] 江大鹏, 杨彪, 邹凌. 基于LBP卷积神经网络的面部表情识别[J]. 计算机工程与设计, 2018, 39(7):1971-1977. (JIANG D P, YANG B, ZOU L. Facial expression recognition based on local binary mode convolution neural network[J]. Computer Engineering and Design, 2018, 39(7):1971-1977.)
[12] SUN K, YIN X, YANG M, et al. The face recognition method based on CS-LBP and DBN[J]. Mathematical Problems in Engineering, 2018, 2018:No. 3620491.
[13] OJALA T, PIETIKÄINEN M, HARWOOD D. A comparative study of texture measures with classification based on featured distributions[J]. Pattern Recognition, 1996, 29(1):51-59.
[14] OJALA T, PIETIKÄINEN M, MAENPAA T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7):971-987.
[15] HEIKKLÄ M, PIETIKÄINEN M. A texture-based method for modeling the background and detection moving objects[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(4):657-662.
[16] HEIKKILÄ M, PIETIKÄINEN M, SCHMID C. Description of interest regions with local binary patterns[J]. Pattern Recognition, 2009, 42(3):425-436.
[17] TAN X, TRIGGS B. Enhanced local texture feature sets for face recognition under difficult lighting conditions[J]. IEEE Transactions on Image Processing, 2010, 19(6):1635-1650.
[18] GUO Z, ZHANG L, ZHANG D. Rotation invariant texture classification using LBP Variance (LBPV) with global matching[J]. Pattern Recognition, 2010, 43(3):706-719.
[19] 王燕, 王芸芸. 一种DCT和ELBP融合的人脸特征提取方法[J]. 计算机工程与应用, 2017, 53(4):170-175. (WANG Y, WANG Y Y. Face feature extraction method based on fusing DCT and ELBP features[J]. Computer Engineering and Applications, 2017, 53(4):170-175.
[20] WEI X, WANG H, GUO G, et al. Multiplex image representation for enhanced recognition[J]. International Journal of Machine Learning & Cybernetics, 2018, 9(3):383-392.
[21] 张洁玉, 武小川. 加权局部二值模式的人脸特征提取[J]. 中国图象图形学报, 2018, 19(12):1794-1801. (ZHANG J Y, WU X C. Feature extraction of faces based on a weighted local binary pattern[J]. Journal of Image and Graphics, 2018, 19(12):1794-1801.)
[22] 木立生, 吕迎春. 基于稀疏表示与特征融合的人脸识别方法[J]. 现代电子技术, 2018, 41(9):83-86,90. (MU L S, LYU Y C. Face recognition method based on sparse representation and feature fusion[J]. Modern Electronics Technique, 2018, 41(9):83-86,90.)
[23] SHANNON C E. A mathematical theory of communication[J]. Bell Labs Technical Journal, 1948, 27(3):379-423.
[24] AHONEN T, HADID A, PIETIKÄINEN M. Face recognition with local binary patterns[C]//Proceedings of the 2004 European Conference on Computer Vision, LNCS 3021. Berlin:Springer, 2004:469-481.