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, Volume 103, Issue 3, pp 2435–2453 | Cite as

Automatic Live Facial Expression Detection Using Genetic Algorithm with Haar Wavelet Features and SVM

  • Sandeep Kumar
  • Sukhwinder Singh
  • Jagdish Kumar
Article
  • 70 Downloads

Abstract

Facial expression detection (FED) and extraction show the most important role in face recognition. In this research, we proposed a new algorithm for automatic live FED using radial basis function support Haar Wavelet Transform is used for feature extraction and RBF-SVM for classification. Edges of the facial image are detected by genetic algorithm and fuzzy-C-means. The experimental results used CK+ database and JAFEE database for facial expression. The other database used for face detection process namely FEI, LFW-a, CMU + MIT and own database. In this algorithm, the face is detected by fdlibmex technique but we improved the limitations of this algorithm using contrast enhancement. In the pre-processing stage, apply median filtering for removing noise from an image. This stage improves the feature extraction process. Finding an image from the image components is a typical task in pattern recognition. The detection rate has reached up to approximately 100% for expression recognition. The proposed system estimates the value of precision and recall. This algorithm is compared with the previous algorithm and our proposed algorithm is better than previous algorithms.

Keywords

Face detection Median filter Genetic algorithm Haar features Facial expression Support vector machine 

Notes

References

  1. 1.
    Sheikh, C.-S., & Sharma, S. (2016). A Survey paper on face detection and recognization with genetic algorithm. International Journal of Research in Applied Science and Engineering Technology (IJRASET), 4(6), 370–375.Google Scholar
  2. 2.
    Kumar, S., Singh, S., & Kumar, J. (2017). A study on face recognition techniques with age and gender classification. In IEEE international conference on computing, communication and automation (ICCCA).Google Scholar
  3. 3.
    Ma, S., & Bai, L. (2016). A face detection algorithm based on AdaBoost and new Haar-Like feature. In 7th IEEE international conference on software engineering and service science (ICSESS) (pp. 651–654).Google Scholar
  4. 4.
    Saad, I. A., George, L. E., & Tayyar, A. A. (2014). Accurate and fast pupil localization using contrast stretching, seed filling, and circular geometrical constraints. Journal of Computer Science, 10(2), 305–315.CrossRefGoogle Scholar
  5. 5.
    Kumar, S., Singh, S., & Kumar, J. (2017). A comparative study on face spoofing attacks. In IEEE international conference on computing, communication and automation (ICCCA).Google Scholar
  6. 6.
    Kumar, S., Singh, S., & Kumar, J. (2017). Automatic face detection using genetic algorithm for various challenges. International Journal of Scientific Research and Modern Education, 2(1), 197–203.Google Scholar
  7. 7.
    Dey, A. (2016). A contour-based procedure for face detection and tracking from the video. In 3rd IEEE international conference on recent advances in information technology (RAIT) (pp. 483–488).Google Scholar
  8. 8.
    Lin, C.-Y., Fu, J. T., Wang, S.-H., & Huang, C.-L. (2016). New face detection method based on multi-scale histograms. In Second IEEE international conference on multimedia big data (BigMM) (pp. 229–232).Google Scholar
  9. 9.
    Liuliu, W., & Mingyang, L. (2016). Multi-pose face detection research based on AdaBoost. In Eighth IEEE international conference on measuring technology and mechatronics automation (ICMTMA) (pp. 409–412).Google Scholar
  10. 10.
    Chihaoui, M., Elkefi, A., Bellil, W., & Amar, C. B. (2015). Implementation of skin color selection prior to Gabor filter and neural network to reduce the execution time of face detection. In 15th IEEE international conference on intelligent systems design and applications (ISDA) (pp. 341–346).Google Scholar
  11. 11.
    Muttu, Y., & Virani, H. G. (2015). Effective face detection, feature extraction & neural network based approaches for facial expression recognition. In IEEE international conference on information processing (ICIP) (pp. 102–107).Google Scholar
  12. 12.
    Kulkarni, K. R., & Bagal, S. B. (2015). Facial expression recognition. In IEEE international conference on information processing (ICIP) (pp. 535–539).Google Scholar
  13. 13.
    Das, S., & De, S. (2016). Multilevel color image segmentation using modified genetic algorithm (MfGA) inspired fuzzy c-means clustering. In Second IEEE international conference on research in computational intelligence and communication networks (ICRCICN) (pp. 78–83).Google Scholar
  14. 14.
    Kumar, S., Deepika, & Kumar, M. (2017). An improved face detection technique for a long distance and near-infrared images. International Journal of Engineering Research and Modern Education, 2(1), 176–181.Google Scholar
  15. 15.
    Ren, X. (2009). An optimal image thresholding using a genetic algorithm. In IEEE international forum on computer science-technology and applications (IFCSTA’09) (pp. 169–172).Google Scholar
  16. 16.
    Xie, S., & Nie, H. (2013). Retinal vascular image segmentation using genetic algorithm Plus FCM clustering. In Third IEEE international conference on intelligent system design and engineering applications (ISDEA) (pp. 1225–1228).Google Scholar
  17. 17.
  18. 18.
    Sangeetha, Y., Latha, P. M., Narasimhan, Ch., & Prasad, R. S. (2012). Face detection using SMQT techniques. International Journal of Computer Science and Engineering Technology (IJCSET), 2(1), 780–783.Google Scholar
  19. 19.
    Hwang, H., & Haddad, R. A. (1995). Adaptive median filters: New algorithms and results. IEEE Transactions on Image Processing, 4(4), 499–502.CrossRefGoogle Scholar
  20. 20.
    Srinivas, M., & Patnaik, L. M. (1994). Genetic algorithms: A survey. Computer, 27(6), 17–26.CrossRefGoogle Scholar
  21. 21.
    Goldberg, D. E. (1989). Genetic algorithms in search, optimization an machine learning. Boston: Addison-Wesley.zbMATHGoogle Scholar
  22. 22.
    Halder, A., Kar, A., & Pramanik, S. (2012). Histogram-based evolutionary dynamic image segmentation. In 4th international conference on electronics computer technology (pp. 585–589).Google Scholar
  23. 23.
    Wikaisuksakul, S. (2014). A multi-objective genetic algorithm with fuzzy c-means for automatic data clustering. Applied Soft Computing, 24(3), 679–691.CrossRefGoogle Scholar
  24. 24.
    Li, J., Yan, X., & Zhang, D. (2010). Optical braille recognition with haar wavelet features and support-vector machine. In International conference on computer, mechatronics, control and electronic engineering (CMCE) (pp. 64–67).Google Scholar
  25. 25.
    Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273–297.zbMATHGoogle Scholar
  26. 26.
    Hsu, C.-W., & Lin, C.-J. (2002). A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, 13(2), 415–425.CrossRefGoogle Scholar
  27. 27.
    Happy, S. L., & Routray, A. (2015). Automatic facial expression recognition using features of salient facial patches. IEEE Transactions on Affective Computing, 6(1), 1–12.CrossRefGoogle Scholar
  28. 28.
    Viola, P., & Jones, M. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2), 137–154.CrossRefGoogle Scholar
  29. 29.
    Lyons, M. J., Akamatsu, S., Kamachi, M., & Gyoba, J. (1998). Coding facial expressions with Gabor wavelets. In 3rd IEEE international conference on automatic face and gesture recognition (pp. 200–205).Google Scholar
  30. 30.
    Tenorio, E. Z., & Thomaz, C. E. (2011). Analise multilinear discriminate de formas frontalis de imagens 2D deface. In Proceedings of the X Simposio Brasileiro de Automacao Inteligente (SBAI) (pp. 266–271), Universidade Federal de Sao Joao del Rei, Sao Joao del Rei, Minas Gerais, Brazil, 18th–21st 2011.Google Scholar
  31. 31.
    Thomaz, C. E., & Giraldi, G. A. (2010). A new ranking method for principal components analysis and its application to face image analysis. Image and Vision Computing, 28(6), 902–913.CrossRefGoogle Scholar
  32. 32.
    Amaral, V., Figaro-Garcia, C., Gattas, G. J. F., & Thomaz, C. E. (2009). Normalizacao espacial de imagens frontais de face em ambientes controlados e nao-controlados (Vol. 1, no. 1). Periodico Cientifico Eletronico da FATEC Sao Caetano do Sul (FaSCi-Tech) (in Portuguese).Google Scholar
  33. 33.
    Amaral, V., & Thomaz, C. E. (2008) Normalizacao Espacial de Imagens Frontais de Face. Technical report 01/2008, Department of Electrical Engineering, FEI, São Bernardo do Campo, São Paulo, Brazil (in Portuguese).Google Scholar
  34. 34.
    L. L. de Oliveira Jr., & Thomaz, C. E. (2006). Captura e Alinhamento de Imagens: Um Banco de Faces Brasileiro. Undergraduate technical report, Department of Electrical Engineering, FEI, São Bernardo do Campo, São Paulo, Brazil (in Portuguese).Google Scholar
  35. 35.
    Wolf, L., Hassner, T., & Taigman, Y. (2011). Effective face recognition by combining multiple descriptors and learned background statistics. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 33(10), 1978–1990.CrossRefGoogle Scholar
  36. 36.
    Wolf, L., Hassner, T., & Taigman, Y. (2009). Similarity scores based on background samples. In Asian conference on computer vision (ACCV), Xi’an.Google Scholar
  37. 37.
    Taigman, Y., Wolf, L., & Hassner, T. (2009). Multiple one-shots for utilizing class label information. In The British machine vision conference (BMVC), London.Google Scholar
  38. 38.
    Schneiderman, H., & Kanade, T. (2000). A statistical method for 3D object detection applied to faces and cars. In IEEE conference on computer vision and pattern recognition (pp. 746–751).Google Scholar
  39. 39.
    Schneiderman, H., & Kanade, T. (2000). A histogram-based method for detection of faces and cars. In International conference on image processing (pp. 504–507).Google Scholar
  40. 40.
    Schneiderman, H., & Kanade, T. (1998). Probabilistic modeling of local appearance and spatial relationships for object recognition. In IEEE computer society conference on computer vision and pattern recognition (pp. 45–51).Google Scholar
  41. 41.
    Rowley, H. A., Baluja, S., & Kanade, T. (1998). Neural network-based face detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(1), 23–38.CrossRefGoogle Scholar
  42. 42.
    Rowley, H. A., Baluja, S., & Kanade, T. (1998) Rotation invariant neural network-based face detection. In IEEE computer society conference on computer vision and pattern recognition (pp. 38–44).Google Scholar
  43. 43.
    Lucey, P., Cohn, J. F., Kanade, T., Saragih, J., Ambadar, Z., & Matthews, I. (2010). The extended Cohn–Kanade dataset (ck +): A complete dataset for action unit and emotion-specified expression. In IEEE computer society conference on computer vision and pattern recognition-workshops (pp. 94–101).Google Scholar
  44. 44.
    Lajevardi, S. M., & Wu, H. R. (2012). Facial expression recognition in perceptual color space. IEEE Transactions on Image Processing, 21(8), 3721–3732.MathSciNetCrossRefGoogle Scholar
  45. 45.
    Lekdioui, K., Messoussi, R., Ruichek, Y., Chaabi, Y., & Touahni, R. (2017). Facial decomposition for expression recognition using texture/shape descriptors and SVM classifier. Signal Processing: Image Communication.  https://doi.org/10.1016/j.image.2017.08.001.CrossRefGoogle Scholar
  46. 46.
    Vo, D. M., & Le, T. H. (2016). Deep generic features and SVM for facial expression recognition. In 2016 3rd national foundation for science and technology development conference on information and computer science (pp 80–84). IEEE.Google Scholar
  47. 47.
    Zhi, R., Flierl, M., Ruan, Q., & Kleijn, W. B. (2011). Graph-preserving sparse nonnegative matrix factorization with application to facial expression recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 41(1), 38–52.CrossRefGoogle Scholar
  48. 48.
    Zhong, L., Liu, Q., Yang, P., Liu, B., Huang, J., & Metaxas, D. N. (2012). Learning active facial patches for expression analysis. In 2012 IEEE conference on computer vision and pattern recognition (CVPR) (pp. 2562–2569).Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Sandeep Kumar
    • 1
  • Sukhwinder Singh
    • 1
  • Jagdish Kumar
    • 2
  1. 1.ECE DepartmentPEC University of TechnologyChandigarhIndia
  2. 2.EE DepartmentPEC University of TechnologyChandigarhIndia

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