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An SVM-AdaBoost facial expression recognition system

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Abstract

This study is focused on improving the recognition rate and processing time of facial recognition systems. First, the skin is detected by pixel based methods to reduce the searching space for maximum rejection classifier (MRC) which detects the face. The detected face is normalized by a discrete cosine transform (DCT) and down-sampled by Bessel transform. Gabor feature extraction techniques were utilized to extract thousands of facial features that represent facial deformation patterns. An AdaBoost-based hypothesis is formulated to select a few hundreds of Gabor features which are potential candidates for expression recognition. The selected features were fed into a saturated vector machine (SVM) classifier to train it. An average recognition rate of 97.57 % and 92.33 % are registered in JAFFE and Yale databases respectively. The execution time of the proposed method is also significantly lower than others. Generally, the proposed method exhibits superior performance than other methods.

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References

  1. Montagu J (1994) The expression of the passions: the origin and influence of Charles le Brun’s conférence sur l’expression générale et particulière. Yale University Press, Yale

    Google Scholar 

  2. Darwin C, Darwin F, Murray J (1904) The expression of the emotions in man and animals, 2nd edn. London

  3. Ekman P, Friesen WV (1978) The facial action coding system: a technique for the measurement of facial movement. Consulting Psychologists Press, Palo Alto

    Google Scholar 

  4. Ellsworth PC, Smith CA (1988) From appraisal to emotion: differences among umpleasant feelings. Motiv Emot 12:271–302

    Article  Google Scholar 

  5. Bruce V (1993) What the human face tells the human mind: some challenges for the robot-human interface. Adv Robot 8(4):341–355

    Article  MathSciNet  Google Scholar 

  6. Morik K, Brockhausen P, Joachims T (1900) Combining statistical learning with a knowledge-based approach—a case study in intensive care monitoring. In: 16th international conference on machine learning (ICML-99), pp 268–277

    Google Scholar 

  7. Belhumeour PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  8. Wu JX, Brubaker SC, Mullin MD, Rehg JM (2008) Fast asymmetric learning for cascade face detection. IEEE Trans Pattern Anal Mach Intell 30(3):369–382

    Article  Google Scholar 

  9. Delac K, Grgic M, Grgic S (2005) Independent comparative study of PCA, ICA, and LDA on the FERET data set. Int J Imaging Syst Technol 15(5):252–260

    Article  Google Scholar 

  10. Tan X, Chen S, Zhou ZH, Zhang F (2006) Face recognition from a single image per person: a survey. Pattern Recognit 39(9):1725–1745

    Article  MATH  Google Scholar 

  11. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71–86

    Article  Google Scholar 

  12. Al Daoud JE (2009) Enhancement of the face recognition using a modified Fourier-Gabor filter. Int J Adv Soft Comput Appl 1(2)

  13. Mahmoud SA, Al-Khatib WG (2011) Recognition of Arabic (Indian) bank check digits using log-Gabor filters. Appl Intell 35(3):445–456

    Article  Google Scholar 

  14. Ganga MP, Prakash C, Gangashetty SV (2011) Bessel transform for image resizing. In: 18th IEEE international conference on systems, signals and image processing (IWSSIP), pp 1–4

    Google Scholar 

  15. Freund Y, Schapire RE (1995) A decision-theoretic generalization of on-line learning and an application to boosting. In: Computational learning theory, Eurocolt’95, pp 23–37

    Chapter  Google Scholar 

  16. Wang CW, You WH (2013) Boosting-SVM: effective learning with reduced data dimension. Appl Intell 1–10

  17. Chapelle O (2007) Training a support vector machine in the primal. Neural Comput 19(5):1155–1178

    Article  MATH  MathSciNet  Google Scholar 

  18. Zhu J, Rosset S, Zou H, Hastie T (2006) Multi-class adaboost. Ann Arbor 1001(48109):1612

    Google Scholar 

  19. Lee LH, Wan CH, Rajkumar R, Isa D (2012) An enhanced support vector machine classification framework by using Euclidean distance function for text document categorization. Appl Intell 37(1):80–99

    Article  Google Scholar 

  20. Ma L, Khorasani K (2004) Facial expression recognition using constructive feedforward neural networks. IEEE Trans Syst Man Cybern, Part B, Cybern 34(3):1588–1595

    Article  Google Scholar 

  21. Matei O, Pop PC, Vălean H (2013) Optical character recognition in real environments using neural networks and k-nearest neighbor. Appl Intell 1–10

  22. Horiuchi T (1998) Class-selective rejection rule to minimize the maximum distance between selected classes. Pattern Recognit 31(10):1579–1588

    Article  Google Scholar 

  23. Elad M, Hel-Or Y, Keshet R (2002) Rejection based classifier for face detection. Pattern Recognit Lett 23(12):1459–1471

    Article  MATH  Google Scholar 

  24. Phong BT (1975) Illumination for computer generated pictures. ACM, New York

    Google Scholar 

  25. Al-Osaimi FR, Bennamoun M, Mian A (2011) Illumination normalization of facial images by reversing the process of image formation. Mach Vis Appl 22(6):899–911

    Article  Google Scholar 

  26. Chen W, Er MJ, Wu S (2006) Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain. IEEE Trans Syst Man Cybern, Part B, Cybern 36(2):458–466

    Article  Google Scholar 

  27. Pizer SM, Amburn EP (1987) Adaptive histogram equalization and its variations. Comput Vis Graph Image Process 39(3):355–368

    Article  Google Scholar 

  28. Shan S, Gao W, Cao B, Zhao D (2003) Illumination normalization for robust face recognition against varying lighting conditions. In: Proc IEEE workshop on AMFG, pp 157–164

    Google Scholar 

  29. Xie X, Lam KM (2005) Face recognition under varying illumination based on a 2D face shape model. Pattern Recognit 38(2):221–230

    Article  Google Scholar 

  30. Burger W, Burge MJ (2008) Digital image processing. Springer, Berlin

    Book  Google Scholar 

  31. Munoz A, Blu T, Unser M (2001) Least-squares image resizing using finite differences. IEEE Trans Image Process 10(9):1365–1378

    Article  MATH  MathSciNet  Google Scholar 

  32. Pachori RB, Sircar P (2008) EEG signal analysis using FB expansion and second-order linear TVAR process. Signal Process 88(2):415–420

    Article  MATH  Google Scholar 

  33. Valstar MF, Patras I, Pantic M (2005) Facial action unit detection using probabilistic actively learned support vector machines on tracked facial point data. In: IEEE conference on computer vision and pattern recognition, pp 76–84

    Google Scholar 

  34. Zhan YZ, Cheng KY, Chen YB, Wen CJ (2010) A new classifier for facial expression recognition: fuzzy buried Markov model. J Comput Sci Technol 25(3):641–650

    Article  MathSciNet  Google Scholar 

  35. Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425

    Google Scholar 

  36. Weston J, Watkins C (1998) Multi-class support vector machines. Technical report CSD-TR-98-04, Department of Computer Science, Royal Holloway, University of London

  37. Shan C, Gong S, McOwan PW (2009) Facial expression recognition based on local binary patterns: a comprehensive study. Image Vis Comput 27(6):803–816

    Article  Google Scholar 

  38. Lee CC, Huang SS, Shih CY (2010) Facial affect recognition using regularized discriminant analysis-based algorithms. EURASIP J Adv Signal Process 1:96

    Google Scholar 

  39. Lekshmi VP, Sasikumar M (2008) A neural network based facial expression analysis using Gabor wavelets. Proc World Acad Sci, Eng Technol 44:593

    Google Scholar 

  40. Ramanathan R, Nair AS, Sagar VV, Sriram N, Soman KP (2009) A support vector machines approach for efficient facial expression recognition. In: International conference on advances in recent technologies in communication and computing, ARTCom’09, pp 850–854

    Chapter  Google Scholar 

  41. Feng X, Pietikäinen M, Hadid T (2005) Facial expression recognition with local binary patterns and linear programming. Pattern Recognit Image Anal 15(2):546–548

    Google Scholar 

  42. Xiao-xu Q, Wei J (2007) Application of wavelet energy feature in facial expression recognition. In: Proceedings of the IEEE international workshop on anti-counterfeiting, security, identification (ASID ’07), pp 169–174

    Google Scholar 

  43. Liejun W, Xizhong Q, Taiyi Z (2009) Facial expression recognition using improved support vector machine by modifying kernels. Inf Technol J 8(4):595–599

    Article  Google Scholar 

  44. Leonardo F, Treves A (2001) A neural network facial expression recognition system using unsupervised local processing. In: Proceedings of IEEE 2nd international symposium on image and signal processing and analysis, ISPA 2001, pp 628–632

    Google Scholar 

  45. Kumbhar M, Jadhav A, Patil M (2012) Facial expression recognition based on image feature. Int J Comput Commun Eng 1(2):117–119

    Article  Google Scholar 

  46. Kharat GU, Dudul SV (2009) Emotion recognition from facial expression using neural networks. In: Human-computer systems interaction. Springer, Berlin, pp 207–219

    Chapter  Google Scholar 

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Acknowledgements

This paper is supported by the National Nature Science Foundation of China (No. 61272211, 61170126), the Natural Science Foundation of Jiangsu Province (No. BK2011521), and the Research Foundation for Talented Scholars of Jiangsu University (No. 10JDG065).

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Authors and Affiliations

  1. School of Computer Science and Communication Engineering, Jiangsu University, 301 Xuefu Road, 212013, Zhenjiang, Jiangsu, China

    Ebenezer Owusu, Yonzhao Zhan & Qi Rong Mao

Authors
  1. Ebenezer Owusu
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  2. Yonzhao Zhan
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  3. Qi Rong Mao
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Correspondence to Ebenezer Owusu or Yonzhao Zhan.

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Owusu, E., Zhan, Y. & Mao, Q.R. An SVM-AdaBoost facial expression recognition system. Appl Intell 40, 536–545 (2014). https://doi.org/10.1007/s10489-013-0478-9

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