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Automatic facial emotion recognition using weber local descriptor for e-Healthcare system

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Abstract

For an e-Healthcare system, detecting the emotion of a patient is vital for initial assessment of the patient. This paper proposes an emotion recognition system from face using for an e-Healthcare system. For features, Weber local descriptors (WLD) are utilized. In the proposed system, a static facial image is subdivided into many blocks. A multi-scale WLD is applied to each of the blocks to obtain a WLD histogram for the image. The significant bins of the histogram are found by using Fisher discrimination ratio. These bin values represent the descriptors of the face. The face descriptors are then input to a support vector machine based classifier to recognition the emotion. Two publicly available databases are used in the experiments. Experimental results demonstrate that the proposed WLD based system achieves a very high recognition rate, where the highest recognition accuracy reaches up to 99.28 % in case of Cohn–Kanade database.

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References

  1. Pantic, M., Rothkrantz, L.J.M.: Automatic analysis of facial expressions: the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 22(12), 1424–1445 (2000)

    Article  Google Scholar 

  2. Fasel, B., Luettin, J.: Automatic facial expression analysis: a survey. Pattern Recognit. 36(1), 259–275 (2003)

    Article  MATH  Google Scholar 

  3. Valstar, M.F., Mehu, M., Bihan, J., Pantic, M., Scherer, K.: Meta-analysis of the first facial expression recognition challenge. IEEE Trans. Syst. Man Cybern. Part B 42(4), 966–979 (2012)

  4. Zhao, G., Pietikainen, M.: Boosted multi-resolution spatio-temporal descriptors for facial expression recognition. Pattern Recognit. Lett. 30(12), 1117–1127 (2009)

    Article  Google Scholar 

  5. Tian, Y., Kanade, T., Cohn, J.F.: Facial Expression Analysis. Handbook of Face Recognition. Springer, Berlin (2003)

    Google Scholar 

  6. Ekman, P., Friesen, W.: Facial Action Coding System: A Technique for Measurement of Facial Movement. Consulting Psychologists Press, Palo Alto (1978)

    Google Scholar 

  7. Valstar, M., Patras, I., Pantic, M.: Facial action unit detection using probabilistic actively learned support vector machines on tracked facial point data. IEEE Workshop CVPR 3, 76–84 (2005)

    Google Scholar 

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

    Article  Google Scholar 

  9. Turk M.A., Pentland, A.P.: Face recognition using eigenfaces. In: IEEE Workshop CVPR, pp. 586–591. (1991)

  10. Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE Trans. Neural Netw. 13(6), 1450–1464 (2002)

    Article  Google Scholar 

  11. Ojala, T., Pietikainen, M.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  12. Lyons, M.J., Budynek, J., Akamatsu, S.: Automatic classification of single facial images. IEEE Trans. Pattern Anal. Mach. Intell. 21(12), 1357–1362 (1999)

    Article  Google Scholar 

  13. Chen, J., Shan, S., He, C., Zhao, G., Pietikainen, M., Chen, X., Gao, W.: WLD: a robust local image descriptor. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1705–1720 (2010)

    Article  Google Scholar 

  14. Ullah, I., Aboalsamh, H., Hussain, M., Muhammad, G., Bebis, G.: Gender classification from facial images using texture descriptors. J. Internet Technol. 15(5), 801–812 (2014)

    Google Scholar 

  15. Muhammad, G., Hussain, M., Alenezy, F., Bebis, G., Mirza, A.M., Aboalsamh, H.: Race classification from face images using local descriptors. Int. J. Artif. Intell. Tools 21(5), 1250019 (2012)

    Article  Google Scholar 

  16. Muhammad, G.: Multi-scale local texture descriptor for image forgery detection. In: IEEE International Conference on Industrial Technology (ICIT), pp. 1146–1151. IEEE, Cape Town (2013)

  17. Lyons, M.J., Akamatsu, S., Kamachi, M., Gyoba, J.: Coding facial expressions with gabor wavelets. In: IEEE Int. Conf. Automatic Face and Gesture Recognition, pp. 200–205. (1998)

  18. Kanade, T., Cohn, J., Tian, Y.: Comprehensive database for facial expression analysis. In: IEEE Int. Conf. Automatic Face and Gesture Recognition, pp. 46–53. (2000)

  19. Muhammad, G., Melhem, M.: Pathological voice detection and binary classification using MPEG-7 audio features. Biomed. Signal Process. Controls 11, 1–9 (2014)

    Article  Google Scholar 

  20. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(27) (2011); Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm

  21. Muhammad, G., Al-Hammadi, M.H., Hussain, M., Bebis, G.: Image forgery detection using steerable pyramid transform and local binary pattern. Mach. Vision Appl. 25(4), 985–995 (2014)

    Article  Google Scholar 

  22. Niu, Z., Shan, S., Yan, S., Chen, X., Gao, W.: 2D cascaded AdaBoost for eye localization. In: IEEE ICPR’06, pp. 1216–1219. (2006)

  23. Rahulamathavan, Y., Phan, R.C.-W., Chambers, J.A., Parish, D.J.: Facial expression recognition in the encrypted domain based on local fisher discriminant analysis. IEEE Trans. Affect. Comput. 4(1), 83–92 (2013)

    Article  Google Scholar 

  24. Hossain, M.S., Muhammad, G.: Audio-visual emotion recognition using multi-directional regression and ridgelet transform. Springer J. Multimodal User Interfaces (2015). doi:10.1007/s12193-015-0207-2

    Google Scholar 

  25. Zhao, X., Zhang, S.: Facial expression recognition based on local binary patterns and kernel discriminant isomap. Sensors 11, 9573–9588 (2011)

    Article  Google Scholar 

  26. Zhang, Z., Lyons, M., Schuster, M., Akamatsu, S.: Comparison between geometry-based and Gabor-wavelets-based facial expression recognition using multi-layer perceptron. in: Proc. 3rd IEEE Intl. Conf. on Automatic Face and Gesture Recognition, pp. 454–459. 14–16 Apr 1998. doi:10.1109/AFGR.1998.670990

  27. Gao, Y., Leung, M.K.H., Hui, S.C., Tananda, M.W.: Facial expression recognition from line-based caricatures. IEEE Trans. System Man Cybernetics Part A Syst. Humans 33(3), 407–412 (2003)

  28. Masud, M., Hossain, M.S., Alamri, A.: Data interoperability and multimedia content management in e-health systems. IEEE Trans. Inf. Technol. Biomed. 16(6), 1015–1023 (2012)

    Article  Google Scholar 

  29. Cheon, Y., Kim, D.: Natural facial expression recognition using differential-AAM and manifold learning. Pattern Recognit. 42, 1340–1350 (2009)

    Article  MATH  Google Scholar 

  30. Hossain, M.S.: Cloud-supported cyber-physical framework for patients monitoring. IEEE Syst. J. (2015). doi:10.1109/JSYST.2015.2470644

    Google Scholar 

  31. Owusu, E., Zhan, Y., Mao, Q.R.: A neural-Adaboost based facial expression recognition system. Expert Syst. Appl. 41, 3383–3390 (2014)

    Article  Google Scholar 

  32. Kazmi, S.B.: Qurat-ul-Ain, Jaffar, M.A.: Wavelet-based facial expression recognition using a bank of support vector machines. Soft Comput. 16(3), 369–379 (2012)

    Article  Google Scholar 

  33. Hassan, M.M., Hossain, M.S., Alamri, A., Hossain, M.A., Al-Qurishi, M., Aldukhayyil, Y., Ahmed, D.T.: A cloud-based serious games framework for obesity. In: Proc ACM Multimedia CBMAS-EH ’12. Nara, Japan, 2012

  34. Liao, S., Fan, W., Chung, C.S., Yeung, D.-Y.: Facial expression recognition using advanced local binary patterns, tsallis entropies and global appearance features. In: Proc. IEEE Intl. Conf. on Image Processing (ICIP), pp. 665–668. (2006)

  35. Hossain, M.S., Muhammad, G.: Cloud-assisted speech and face recognition framework for health monitoring. Mobile Netw. Appl. 20(3), 391–399 (2015)

    Article  Google Scholar 

  36. Hossain, M.S., Muhammad, G., Song, B., Hassan, M., Alelaiwi, A., Alamri, A.: Audio-visual emotion-aware cloud gaming framework. IEEE Trans. Circuits Syst. Video Technol. 25, 2105–2118 (2015)

    Article  Google Scholar 

  37. El Saddik, A., Hossain, M.S.: Multimedia content repurposing. In: Furht B. (ed.) Encyclopedia of Multimedia. Springer, pp. 494–500. (2006)

  38. Muhammad, G., Fukuda, T., Horikawa, J., Nitta, T.: A noise-robust feature-extraction method based on pitch-synchronous ZCPA for ASR. In: Proc. INTERSPEECH-ICSLP’04, vol. I, pp. 133–136. Korea (2004)

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Acknowledgments

The author extend their appreciation to the Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia for funding this work through the research group Project No. RG-1436-016.

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  1. Department of Computer Engineering, College of Computer & Information Sciences, King Saud University, Riyadh, Saudi Arabia

    Musaed Alhussein

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  1. Musaed Alhussein
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Correspondence to Musaed Alhussein.

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Alhussein, M. Automatic facial emotion recognition using weber local descriptor for e-Healthcare system. Cluster Comput 19, 99–108 (2016). https://doi.org/10.1007/s10586-016-0535-3

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