多尺度局部二值模式傅里叶直方图特征的表情识别

doi:10.11772/j.issn.1001-9081.2014.07.2036

计算机应用 ›› 2014, Vol. 34 ›› Issue (7): 2036-2039.DOI: 10.11772/j.issn.1001-9081.2014.07.2036

多尺度局部二值模式傅里叶直方图特征的表情识别

王丽,李瑞峰,王珂

机器人技术与系统国家重点实验室(哈尔滨工业大学)，哈尔滨 150001

收稿日期:2014-01-26 修回日期:2014-03-12 发布日期:2014-08-01 出版日期:2014-07-01
通讯作者: 王丽
作者简介:王丽(1982-)，女，河北石家庄人，博士研究生，主要研究方向：计算机视觉、图像处理、模式识别；李瑞峰(1965-)，山西大同人，教授，博士，主要研究方向：工业机器人、智能服务机器人、仿人机器人；王珂(1979-)男，黑龙江哈尔滨人，讲师，博士，主要研究方向：计算机视觉、图像处理、人机交互、估计理论及其在机器人中的应用。
基金资助:
国家自然科学基金资助项目

Multi-scale local binary pattern fourier histogram features for facial expression recognition

WANG Li,LI Ruifeng,WANG Ke

State Key Laboratory of Robotics and System (Harbin Institute of Technology), Harbin Heilongjiang 150001, China

Received:2014-01-26 Revised:2014-03-12 Online:2014-08-01 Published:2014-07-01
Contact: WANG Li

摘要/Abstract

摘要：

针对表情识别的简便快捷问题，提出一种多尺度局部二值模式傅里叶直方图(LBP-HF)和主动形状模型(ASM)相结合的人脸表情识别方法。该方法首先利用ASM检测并分割人脸区域，减少不相关区域的影响; 然后提取多尺度LBP-HF特征形成识别向量; 最后采用最近邻分类方法进行表情识别。通过提取不同尺度的LBP-HF特征，研究各个尺度LBP-HF特征对表情识别的影响，最终结合多尺度LBP-HF特征实现表情识别，获得更有效的表情特征。通过与Gabor特征的实验结果进行对比，验证该方法的简便可行性，最高平均识别率达到93.5%。实验结果表明，该方法可以用于人机交互中。

Abstract:

To achieve simple and convenient facial expression recognition, a method combining multi-scale Local Binary Pattern Histogram Fourier (LBP-HF) and Active Shape Model (ASM) was proposed. Firstly, the face regions were detected and segmented by ASM to reduce the influence of unrelated regions, and then LBP-HF were extracted to form recognition vectors. Finally, the nearest neighborhood classifier was applied to recognize expressions. The influences of various scale LBP-HF features on facial expression recognition were studied through extracting LBP-HF features from different scales. At last, multi-scale LBP-HF features were concatenated to discriminate expressions, and more effective expression features were obtained. By comparison with the experimental result of Gabor features, its feasibility and simplication are validated, and the highest mean recognition rate is 93.50%. The experimental results demonstrate that the method can be used for human-computer interaction.

中图分类号:

TP391.41

王丽李瑞峰王珂. 多尺度局部二值模式傅里叶直方图特征的表情识别[J]. 计算机应用, 2014, 34(7): 2036-2039.

WANG Li LI Ruifeng WANG Ke. Multi-scale local binary pattern fourier histogram features for facial expression recognition[J]. Journal of Computer Applications, 2014, 34(7): 2036-2039.

参考文献

[1]MEHRABIAN A. Communication without words ［J］. Psychological Today, 1968, 2(4):53-56.
[2]FASEL B, LUETTIN J. Automatic facial expression analysis: a survey ［J］. Pattern Recognition, 2003, 36(1): 259-275.
[3]YANG M-H, KRIEGMAN D J, AHUJA N. Detecting faces in images: a survey ［J］. IEEE Transactions on Pattern Analysis and Machine Learning, 2002, 24(1): 34-58.
[4]PANTIC M, ROTHKRANTZ L J M. Automatic analysis of facial expressions: the state of the art［J］. IEEE Transactions on Pattern Analysis and Machine Learning, 2000, 22(12): 1424-1445.
[5]JAIN A K, DUIN R P W, MAO J. Statistical pattern recognition: a review ［J］. IEEE Transactions on Pattern Analysis and Machine Learning, 2000, 22(1): 4-37.
[6]ZHAN Y, YE J, NIU D, et al. Facial expression recognition based on Gabor wavelet transformation and elastic template matching［C］// Proceedings of the 3rd International Conference on Image and Graphics. Washington, DC: IEEE Computer Society, 2004: 254-257.
[7]LI Z, IMAI J, KANEKO M. Facial-component-based bag of words and PHOG descriptor for facial expression recognition［C］// Proceedings of the 2009 IEEE International Conference on System, Man, and Cybernetics. Piscataway: IEEE, 2009: 1353-1358.
[8]RUDOVIC O, PANTIC M, PATRAS I. Coupled Gaussian processes for pose-invariant facial expression recognition［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(6):1357-1369.
[9]RIVERA A R, CASTILL J R, CHAE O. Local directional number pattern for face analysis: face and expression recognition ［J］. IEEE Transactions on Image Processing, 2013, 22(5):1740-1752.
[10]ZHOU S, LIANG X. Facial expression recognition algorithm fused ICA and support vector clustering［J］. Journal of Computer Applications, 2011, 31(6): 1065-1068.(周书仁，梁昔明. 融合独立分量分析与支持向量机聚类的人脸表情识别方法［J］.计算机应用,2011,31(6):1065-1068.)
[11]LIAO C, CHUANG H, DUAN C H, et al. Learning spatial weighting for facial expression analysis via constrained quadratic programming［J］. Pattern Recognition, 2013,46(11): 3103-3116.
[12]TAWARI A, TRIVEDI M M. Face expression recognition by cross modal data association［J］.IEEE Transactions on Multimedia, 2013,15(7):1543-1552.
[13]HUANG D, SHAN C, ARDABILIAN M. Local binary patterns and its application to facial image analysis: a survey［J］. IEEE Transactions on System, Man, and Cybernetics- Part C: Application and Reviews, 2011,41(5):765-781.
[14]OJALA T, PIETIKINEN M, HARWOOD D. A comparative study of texture measures with classification based on feature distributions［J］.Pattern Recognition,1996,29(1):51-59.
[15]OJALA T, PIETIKINEN M, MENP T. Multiresolution gray scale and rotation invaraint texture analysis with local binary patterns［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7):971-987.
[16]ZHAO G, AHONEN T, MATAS J, et al. Rotation-invariant image and video descriptor with local binary pattern features［J］. IEEE Transactions on Image Processing, 2012, 21(4):1465-1477.
[17]BALDOCK E R, GRAHAM J. Image processing and analysis［M］. Oxford: Oxford University Press, 2000.
[18]AHONEN T, HADID A, PIETIKINEN M. Face description with local binary patterns: Application to face recognition［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006,28(12):2037-2041.
[19]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.
[20]BASHYAL S, VENAYAGAMOORTHY G K. Recognition of facial expressions using Gabor wavelets and learning vector quantization［J］. Engineering Applications of Artificial Intelligence, 2008,21(7):1056-1064.

多尺度局部二值模式傅里叶直方图特征的表情识别

Multi-scale local binary pattern fourier histogram features for facial expression recognition

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