Emotion Recognition System Based on Facial Expressions Using SVM

  • Ielaf Osaamah Abdul-MajjedEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 555)


In recent years, there has been a growing interest in improving all aspects of interactions between human and computers particularly in the area of human expression recognition. In this work, we design a robust system by combining various techniques from computer vision and pattern recognition. Our system is divided into four modules started with image preprocessing, followed by feature extraction including Prewitt. Subsequently, the feature selection module has been performed as a third step using the sequential forward selection (SFS). Finally, support vector machine (SVM) is used as a classifier. Training and testing have been done on the Radboud Faces Database. The goal of the system was attaining the highest possible classification rate for the seven facial expressions (neutral, happy, sad, surprise, anger, disgust, and fear). 75.8% recognition accuracy was achieved on training dataset.


Facial expressions SVM SFS Emotion recognition 


  1. 1.
    Langner, O., Dotsch, R., Bijlstra, G., Wigboldus, D.H.J., Hawk, S.T., & van Knippenberg, A. Presentation and validation of the Radboud Faces Database. Cognition & Emotion, 24(8), 1377–1388, 2010.Google Scholar
  2. 2.
    J. P. Skelley, “Experiments in Expression Recognition,” MSc Thesis, Massachusetts institute of technology, 2005.Google Scholar
  3. 3.
    Zilu Ying, Mingwei Huang, Zhen Wang, and Zhewei Wang, “A New Method of Facial Expression Recognition Based on SPE Plus SVM,” Springer-Verlag Berlin Heidelberg, Part II, CCIS 135, 2011, pp. 399–404.Google Scholar
  4. 4.
    M. Pantic, and I. Patras, “Dynamics of Facial Expression: Recognition of Facial Action and their Temporal Segment From Face Profile Image sequences,” IEEE Trans. System, Man, and Cybernetic, Part B, Vol. 36, No. 2, 2006, pp. 433–449.Google Scholar
  5. 5.
    I. Kotsia, I. Buciu, and I. Pitas, “An Analysis of Facial Expression Recognition under Partial Facial Image Occlusion,” Image and Vision Computing, Vol. 26, No. 7, 2008, pp. 1052–1067.Google Scholar
  6. 6.
    SF Pratama, AK Muda, YH Choo, NA Muda‏. “Computationally Inexpensive Sequential Forward Floating Selection for Acquiring Significant Features for Authorship,” International Journal on New Computer Architectures and Their Applications (IJNCAA) 1(3): 581–598 The Society of Digital Information and Wireless Communications, 2011 (ISSN: 2220-9085).Google Scholar
  7. 7.
    V. N. Vapnik. “The Nature of Statistical Learning Theory”. Springer, New York, 2nd edition, 2000.Google Scholar
  8. 8.
    S. V. N. Vishwanathan and M. N. Murty. “SSVM: A simple SVM algorithm”, in Proceedings of IJCNN, IEEE Press, 2002.Google Scholar
  9. 9.
    Cortes C, Vapnik V. “Support-vector networks,” Mach Learn, 1995, 20(3):273–297.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Computer Science, College of Computer Science and MathematicsUniversity of MosulMosulIraq

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