基于稀疏表示残差融合的人脸表情识别
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摘要
人脸表情识别是计算机由人脸图像中提取表情特征,然后根据特征的不同将表情图像归到不同的表情类别中。它使得计算机能够根据表情图像分类结果,推断人的心理状态,从而实现人机之问自然交互。尽管目前人脸表情识别技术已经取得了不少进展,但现实生活中光照、姿态、噪声、遮掩物等各种因素影响,要实现真正实际应用仍需进一步研究。
本文综述了人脸表情识别的一般步骤,及各个步骤中所利用到的方法,介绍了压缩传感CS (Compressed Sensing)的理论方法以及在此基础上稀疏表示分类算法SRC (Sparse Representation Classification),将SRC算法用于表情识别,进行了一系列的实验。本论文的主要研究工作包括下面几个方面。
1.研究了稀疏表示在人脸表情识别上的应用。将SRC算法应用在日本女性表情JAFFE库上,在特定人识别情况下与2DPCA+SVM的方法进行比较。实验结果表明了SRC算法的有效性。比较2DPCA或Curvelet降维后进行SRC分类与随机映射后进行SRC分类,验证了稀疏表示对特征提取方法的不敏感性。
2.研究了稀疏表示在噪声条件下的分类能力。实验在JAFFE表情库上进行,在测试图像上加入方差从0.01至0.1的高斯噪声,运用SRC与2DPCA+SVM和Curvelet+SVM等方法来进行表情识别,并通过上述方法结果的比较,验证了SRC算法的鲁棒性。
3.研究了稀疏表示在遮挡条件下的分类能力。在JAFFE表情库中的表情图像中加入了遮挡情况下进行表情识别。一幅与原图像不相关的图像作为遮挡替代了原图像的一部分,遮挡面积从10%至50%,通过SRC,2DPCA+SVM和Curvelet+SVM等方法进行分类,实验结果表明了SRC算法具有很好对遮挡的鲁棒性。
4.提出了基于稀疏表示残差融合的人脸表情识别算法。根据SRC算法在灰度与局部二元模式(LBP)两种特征上分类的残差分析,提出了一种融合残差信息的表情识别新方法。为证明这种方法的有效性,将这种方法分别应用于JAFFE表情库和CK表情库,结果表明新方法的识别率在JAFFE中达到了69.52%,远高于单独SRC的62.43%和LBP+SRC的60.52%,识别率提高了8%左右；在CK表情库中的实验表明,新方法比单独的SRC或LBP+SRC提高了5%左右。
Facial expression recognition (FER) is about using computer to extract facial features of all expression images, and then classify the images to different expression categories according to those facial features. FER makes computer know the expression states from the classification result and achieve Human-Computer Natural Interaction. Although much progress has been made in FER in the past years, many methods need to be researched for purpose of increasing the recognition rate on with the influence of illumination, posture, noise, masking, and so on.
In this paper, we give a survey of the common steps of FER and the methods applied in every step. Compressed sensing (CS) is introduced in this paper as well as the Sparse Representation Classification (SRC) algorithm. SRC is used in FER and experiments are carried out on facial expression database. The research work in this paper mainly includes the following.
1. SRC is used in FER, and compared with 2DPCA+SVM in JAFFE facial expression database in FER depends on person. The result shows that SRC is efficient. Compared with 2DPCA+SRC, Curvelet+SRC and Random Projection+SRC, the result shows that the role of feature extraction is not so important as in common approach.
2. SRC is used in facial expression recognition of the image added with noise. The variance of the white noise form 0.01 to 0.1. The experiment is carried out in JAFFE and compared with 2DPCA+SVM and Curvelet+SVM. The result shows that SRC is robust with noise and performs best in these methods.
3. SRC is used in facial expression recognition of the image with blocks. The experiment is carried out in JAFFE and some part of an image of facial expression is replaced by an unrelated image. The replaced area is from 10% to 50%. SRC is compared with 2DPCA+SVM and Curvelet+SVM. The result shows that SRC is robust with block.
4. A new approach for facial expression recognition based on fusion of sparse representation is proposed. The gray information is used in the method of SRC, and the texture information is used by LBP. These two methods could fusion by analyze the residuals. The result of the experiment in JAFFE shows that the recognition rate of the new fusion approach is 69.52 which is much high than 62.43% of SRC and 60.52% of LBP+SRC. This new approach also used in CK facial expression database. The result of the experiment shows that the recognition rate is 5% higher than SRC or LBP+SRC.
本文综述了人脸表情识别的一般步骤,及各个步骤中所利用到的方法,介绍了压缩传感CS (Compressed Sensing)的理论方法以及在此基础上稀疏表示分类算法SRC (Sparse Representation Classification),将SRC算法用于表情识别,进行了一系列的实验。本论文的主要研究工作包括下面几个方面。
1.研究了稀疏表示在人脸表情识别上的应用。将SRC算法应用在日本女性表情JAFFE库上,在特定人识别情况下与2DPCA+SVM的方法进行比较。实验结果表明了SRC算法的有效性。比较2DPCA或Curvelet降维后进行SRC分类与随机映射后进行SRC分类,验证了稀疏表示对特征提取方法的不敏感性。
2.研究了稀疏表示在噪声条件下的分类能力。实验在JAFFE表情库上进行,在测试图像上加入方差从0.01至0.1的高斯噪声,运用SRC与2DPCA+SVM和Curvelet+SVM等方法来进行表情识别,并通过上述方法结果的比较,验证了SRC算法的鲁棒性。
3.研究了稀疏表示在遮挡条件下的分类能力。在JAFFE表情库中的表情图像中加入了遮挡情况下进行表情识别。一幅与原图像不相关的图像作为遮挡替代了原图像的一部分,遮挡面积从10%至50%,通过SRC,2DPCA+SVM和Curvelet+SVM等方法进行分类,实验结果表明了SRC算法具有很好对遮挡的鲁棒性。
4.提出了基于稀疏表示残差融合的人脸表情识别算法。根据SRC算法在灰度与局部二元模式(LBP)两种特征上分类的残差分析,提出了一种融合残差信息的表情识别新方法。为证明这种方法的有效性,将这种方法分别应用于JAFFE表情库和CK表情库,结果表明新方法的识别率在JAFFE中达到了69.52%,远高于单独SRC的62.43%和LBP+SRC的60.52%,识别率提高了8%左右；在CK表情库中的实验表明,新方法比单独的SRC或LBP+SRC提高了5%左右。
Facial expression recognition (FER) is about using computer to extract facial features of all expression images, and then classify the images to different expression categories according to those facial features. FER makes computer know the expression states from the classification result and achieve Human-Computer Natural Interaction. Although much progress has been made in FER in the past years, many methods need to be researched for purpose of increasing the recognition rate on with the influence of illumination, posture, noise, masking, and so on.
In this paper, we give a survey of the common steps of FER and the methods applied in every step. Compressed sensing (CS) is introduced in this paper as well as the Sparse Representation Classification (SRC) algorithm. SRC is used in FER and experiments are carried out on facial expression database. The research work in this paper mainly includes the following.
1. SRC is used in FER, and compared with 2DPCA+SVM in JAFFE facial expression database in FER depends on person. The result shows that SRC is efficient. Compared with 2DPCA+SRC, Curvelet+SRC and Random Projection+SRC, the result shows that the role of feature extraction is not so important as in common approach.
2. SRC is used in facial expression recognition of the image added with noise. The variance of the white noise form 0.01 to 0.1. The experiment is carried out in JAFFE and compared with 2DPCA+SVM and Curvelet+SVM. The result shows that SRC is robust with noise and performs best in these methods.
3. SRC is used in facial expression recognition of the image with blocks. The experiment is carried out in JAFFE and some part of an image of facial expression is replaced by an unrelated image. The replaced area is from 10% to 50%. SRC is compared with 2DPCA+SVM and Curvelet+SVM. The result shows that SRC is robust with block.
4. A new approach for facial expression recognition based on fusion of sparse representation is proposed. The gray information is used in the method of SRC, and the texture information is used by LBP. These two methods could fusion by analyze the residuals. The result of the experiment in JAFFE shows that the recognition rate of the new fusion approach is 69.52 which is much high than 62.43% of SRC and 60.52% of LBP+SRC. This new approach also used in CK facial expression database. The result of the experiment shows that the recognition rate is 5% higher than SRC or LBP+SRC.
引文
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[31]Candes, Tao T. Decoding by Linear Programming [J]. IEEE Transactions on Information Theory.2005,51(12):4203-4215.
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[39]Sebert F, Zou Y M, Ying L. Toeplitz Block Matrices in Compressed Sensing and Their Applications in Imaging [J]. Proceedings of International Conference on Technology and Applications in Biomedicine. Washington D. C., USA:IEEE,2008. 47-50.
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[43]Kim S, Koh K, Lustig M, Boyd S, Gorinevsky D. An Interior Point Method for Large-scale 11 Regularized Least Squares [J]. IEEE Journal of Selected Topics in Signal Processing,2007,1(4):606-617.
[44]Fiqueiredo M A T, Nowak R D, Wright S J. Gradient Projection for Sparse Reconstruction:Application to Compressed Sensing and Other Inverse Problems [J]. IEEE Journal of Selected Topics in Signal Processing,2007,1(4):586-598.
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[2]Haisong G, Qiang J, Zhiwei Zh. Active Facial Tracking for Fatigue Detection [A]. Proc. 6th IEEE WACV, Orlando, Florida, USA,2002,
[3]Darwin C. The Expression of the Emotions in Man and Animals [M]. J. Murray, London,1872.
[4]Ekamn P, Friesen W V. Facial Action Coding System (FACS):Manual [M].Consulting Psychologists Press,1978.
[5]梁路宏,艾海舟.人脸检测研究综述[J].计算机学报,2005,5：449-458.
[6]Wang J G, Tan T N. A New Face Detection Method Based on Shape Information [J]. Pattern Recognition Letters,2000,21(6):463-471.
[7]Yang G Z, Huang T S. Human Face Detection in A Complex Background [J]. Pattern Recognition,1994,27(1):53-63.
[8]王志良,陈锋军,薛为民.人脸表情识别方法综述[J].计算机学报,2002,8：223-235.
[9]刘晓旻,章毓晋.基于Gabor直方图特征和MVBoost的人脸表情识别[J].计算机研究与发展.2007,44(7)：1089.1096.
[10]刘晓旻,谭华春,章毓晋.人脸表情识别研究的新进展[J].中国图形图像学报.2006,11(10)：1359-1368.
[11]余棉水,黎绍发.基于光流的动态人脸表情识别[J].微电子学与计算机.2005,22(17)：113-119.
[12]邓洪波,金连文.一种基于局部Gabor滤波器组及PCA+LDA的人脸表情识别方法[J].中国图形图像学报.2007.2：322-329.
[13]董戎萍,唐伯良.基于DCT- BP神经网络的人脸表情识别[J].微计算机信息.2005.12：142-144.
[14]王志堂,蔡淋波.隐马尔可夫模型(HMM)及其应用[J].湖南科技学院学报.2009.3：42-44.
[15]Donoho DL. Compressed Sensing [J]. IEEE Transactionson Information Theory, 2006,52(4):1289-1306.
[16]Candes. Compressive Sampling [J]. Proceedings of International Congress of Mathematicians. Zurich, Switzerland:European, Mathematical Society Publishing House,2006.1433-1452.
[17]Baraniuk R. Compressive Sensing [J]. IEEE Signal Processing Magazine,2007, 24(4):118-121.
[18]Olshausen B A, Field D J. Emergence of Simple-cell Receptive Field Properties by Learning a Sparse Code for Natural Images [J]. Nature,1996,381(6583):607-609.
[19]Mallat S. A Wavelet Tour of Signal Processing [M]. San Diego, USA:Academic Press,1996.
[20]Candes, Donoho D L. Curvelets A Surprisingly Effective Nonadaptive Representation For Objects With Edges [R], Technical Report, Department of Statistics, Stanford University, USA,1999.
[21]孙玉宝,肖亮,韦志辉,邵文泽.基于Gabor感知多成分字典的图像稀疏表示算法研究[J].自动化学报,2008,34(11):1379-1378.
[22]Aharon M, Elad M, Bruckstein A M. The K-SVD:Algorithm For Designing of Overcomplete Dictionaries For Sparse Representations [J]. IEEE Transactions on Image Processing,2006,54(11):4311-4322.
[23]Rauhut H, Schnass K V P. Compressed Sensing and Redundant Dictionaries [J]. IEEE Transactions on Information Theory,2008,54(5):2210-2219.
[24]Candes, Romberg J. Sparsity And Incoherence in Compressive Sampling [J].Inverse Problems,2007,23(3):969-985.
[25]Candes, Romberg J,Tao T. Robust Uncertainty Principles:Exact Signal Reconstruction From Highly Incomplete Frequency Information [J]. IEEE Transactions on Information Theory.2006,52(2):489-509.
[26]Candes, Braun N, Wakin M B. Sparse Signal And Image Recovery From Compressive Samples [J]. Proceedings of the 4th IEEE International Symposium on Biomedical Imaging:From Nano to Macro. Washington D.C., USA:IEEE, 2007.976-979.
[27]Tropp J, Gilbert A. Signal Recovery from Random Measurements via Orthogonal Matching Pursuit [J]. Transactionson Information Theory,2007,53(12):4655-4666.
[28]Blumensath T, Davies M E. Iterative Rhresholding for Sparse Approximations [J]. Journal of Fourier Analysis and Applications,2007,14(5-6):629-654.
[29]Huang K, Aviyente S. Sparse Representation for Signal Classification [J], Neural Information Processing Systems,2006.
[30]Wright J, Yang A Y, Ganesh A, Sastry S, and Ma Y. Robust face recognition via sparse representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence 2009,31(2):210-227.
[31]Candes, Tao T. Decoding by Linear Programming [J]. IEEE Transactions on Information Theory.2005,51(12):4203-4215.
[32]Candes, Romberg J, Tao T. Stable Signal Recovery from Incomplete and Inaccurate Measurements [J]. Communications on Pure and Applied Mathematics,2006,59(8): 1207-1223.
[33]Candes, Tao T. Near Optimal Signal Recovery from Random Projections:Universal Encoding Strategies? [J] IEEE Transactions on Information Theory,2006,52(12): 5406-5426.
[34]Hong F, Quan-Bing Zh, Shui W. A Method of Image Reconstruction Based on Sub-Gussian Random Projection [J]. Journal of Computer Research and Development,2008,45(8):1402-1407.
[35]Gilbert A C, Guha S, Indyk P. Near-optimal Sparse Fourier Representations via Sampling [J]. Proceedings of the 34th Annual ACM Symposium on Theory of Computing.Quebec, Canada.2006.152-161.
[36]Tsaig Y, Donoho D. Extensions of Compressed Sensing [J]. Signal Processing,2006, 86(3):549-571.
[37]Pinkus A. N-widths in Approximation Theory [J]. New York:Springer-Verlag,1985.
[38]Bajwa W, Haupt J, Raz G, Wright S J, Nowak R D. Toeplitz-structured Compressed Sensing Matrices [J]. Pro-ceedings of the IEEE Workshop on Statistical Signal Pro-cessing. Washington D. C., USA:IEEE,2007.294-298.
[39]Sebert F, Zou Y M, Ying L. Toeplitz Block Matrices in Compressed Sensing and Their Applications in Imaging [J]. Proceedings of International Conference on Technology and Applications in Biomedicine. Washington D. C., USA:IEEE,2008. 47-50.
[40]Do T T, Trany T D, Gan L. Fast Compressive Sampling with Structurally Random Matrices [J]. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing. Washington D. C., USA:IEEE,2008.3369-3372.
[41]Chen S B, Donoho D L, Saunders M A. Atomic decomposition by basis pursuit [J]. SIAM Journal on Scientific Computing,1998,20(1):33-61.
[42]Donoho D L, Elad M, Temlyakov V. Stable Recovery of Sparse Overcomplete Representations in Presence of Noise [J]. IEEE Transactions on Information Theory, 2006,52(1):6-18.
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