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Reseach Article

A Real-Time System for Facial Expression Recognition using Support Vector Machines and k-Nearest Neighbor Classifier

by Hend Ab. ELLaban, A. A. Ewees, Elsaeed E. AbdElrazek
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 159 - Number 8
Year of Publication: 2017
Authors: Hend Ab. ELLaban, A. A. Ewees, Elsaeed E. AbdElrazek
10.5120/ijca2017913009

Hend Ab. ELLaban, A. A. Ewees, Elsaeed E. AbdElrazek . A Real-Time System for Facial Expression Recognition using Support Vector Machines and k-Nearest Neighbor Classifier. International Journal of Computer Applications. 159, 8 ( Feb 2017), 23-29. DOI=10.5120/ijca2017913009

@article{ 10.5120/ijca2017913009,
author = { Hend Ab. ELLaban, A. A. Ewees, Elsaeed E. AbdElrazek },
title = { A Real-Time System for Facial Expression Recognition using Support Vector Machines and k-Nearest Neighbor Classifier },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2017 },
volume = { 159 },
number = { 8 },
month = { Feb },
year = { 2017 },
issn = { 0975-8887 },
pages = { 23-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume159/number8/27022-2017913009/ },
doi = { 10.5120/ijca2017913009 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:05:14.497330+05:30
%A Hend Ab. ELLaban
%A A. A. Ewees
%A Elsaeed E. AbdElrazek
%T A Real-Time System for Facial Expression Recognition using Support Vector Machines and k-Nearest Neighbor Classifier
%J International Journal of Computer Applications
%@ 0975-8887
%V 159
%N 8
%P 23-29
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Faces are a unique feature of human being that can detect a great deal of information about age, health, personalities and feelings. Facial Expressions are the main sources in determining the internal impressions of the individual. Real-Time system for facial expression recognition is able to detect and locate human faces in image sequences obtained in real environments then extracts expression features from these images finally recognize facial expressions. In this paper, the proposed system presents a real-time system for facial expression recognition that aims to recognize 8 basic facial expressions of students: anger, disgust, fear, happy, nervous, sad, surprise and natural inside E-learning environment. The primary objective is to use k-NN and SVM classifiers to test the efficiency of the proposed system and compared the results of them. There are some techniques has been used in this study for facial expression recognition such as Viola-Jones approaches to detect a face from images, Gabor Feature approach to extract features, and Principal Component Analysis (PCA) to select features and k-NN, SVM classifiers to recognize expressions from facial image.. The result showed that the SVM classifier has the best recognition rate in general thank-NN classifier. From these results, it can say that SVM classifier is more suitable for recognition of facial expression in a real-time system.

References
  1. Ekman, P., & Friesen, W. V. (1978). The facial action coding system: a technique for the measurement of facial movement. Consulting Psychologists Press, Palo Alto.
  2. Alazrai, R., & Lee, C. G. (2012, May). Real-time emotion identification for socially intelligent robots. In Robotics and Automation (ICRA), 2012 IEEE International Conference on (pp. 4106-4111). IEEE.
  3. Lau, B. T. (2010). Portable real time emotion detection system for the disabled. Expert Systems with Applications, 37(9), 6561-6566.
  4. Lu, X. (2003). Image analysis for face recognition. personal notes, May, 5.
  5. Grafsgaard, J., Wiggins, J. B., Boyer, K. E., Wiebe, E. N., & Lester, J. (2013, July). Automatically recognizing facial expression: Predicting engagement and frustration. In Educational Data Mining 2013.
  6. Littlewort, G., Whitehill, J., Wu, T., Fasel, I., Frank, M., Movellan, J., & Bartlett, M. (2011, March). The computer expression recognition toolbox (CERT). In Automatic Face & Gesture Recognition and Workshops (FG 2011), 2011 IEEE International Conference on (pp. 298-305). IEEE.
  7. Sathik, M., & Jonathan, S. G. (2013). Effect of facial expressions on student’s comprehension recognition in virtual educational environments. Springer Plus, 2(1), 1.
  8. Loh, M. P., Wong, Y. P., & Wong, C. O. (2005, July). Facial expression analysis in e-learning systems-the problems and feasibility. In Fifth IEEE International Conference on Advanced Learning Technologies (ICALT'05) (pp. 442-446). IEEE.
  9. Peng, Z. Y., Wen, Z. Q., & Zhou, Y. (2009, January). Application of mean shift algorithm in real-time facial expression recognition. In Computer Network and Multimedia Technology, 2009. CNMT 2009. International Symposium on (pp. 1-4). IEEE.
  10. Zhao-yi, P., Yan-hui, Z., & Yu, Z. (2010, April). Real-time facial expression recognition based on adaptive canny operator edge detection. In Multimedia and Information Technology (MMIT), 2010 Second International Conference on (Vol. 2, pp. 154-157). IEEE.
  11. N. Bajaj and A. Routray (2013): "Dynamic Model of Facial Expression Recognition based on Eigen-face Approach", Proceedings of Green Energy and Systems Conference 2013, November 25, Long Beach, CA, USA
  12. Srivastava, S. (2012, January). Real Time Facial Expression Recognition. In International Conference on Computer Science and Information Technology (pp. 124-133). Springer Berlin Heidelberg.
  13. Wang, Y., Ai, H., Wu, B., & Huang, C. (2004, August). Real time facial expression recognition with adaboost. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on (Vol. 3, pp. 926-929). IEEE.
  14. Geetha, A., Ramalingam, V., Palanivel, S., & Palaniappan, B. (2009). Facial expression recognition–A real time approach. Expert Systems with Applications, 36(1), 303-308.
  15. Adeshina, A.M., Lau, S.-H., Loo, C.-K. (2009). Real-time FERs: a review. Innovative Technologies Intelligent Systems and Industrial Applications, pp. 375–378
  16. Happy, S. L., George, A., & Routray, A. (2012, December). A real time facial expression classification system using Local Binary Patterns. In Intelligent Human Computer Interaction (IHCI), 2012 4th International Conference on (pp. 1-5). IEEE.
  17. Dalal, N., & Triggs, B. (2005, June). Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) (Vol. 1, pp. 886-893). IEEE.
  18. Jabid, T., Kabir, M. H., & Chae, O. (2010, September). Facial expression recognition using local directional pattern (LDP). In 2010 IEEE International Conference on Image Processing (pp. 1605-1608). IEEE.
  19. Verma, D., Saini, L.K., Joshi, K. (2013). Performance analysis of feature extraction technique for facial expression recognition. Int. J. Adv. Comput. Technol. (IJACT), 2(4), pp. 16–20
  20. Ahn, B., Han, Y., & Kweon, I. S. (2012, November). Real-time facial landmarks tracking using active shape model and lk optical flow. In Ubiquitous Robots and Ambient Intelligence (URAI), 2012 9th International Conference on (pp. 541-543). IEEE.
  21. Wu, T., Fu, S., & Yang, G. (2012, July). Survey of the facial expression recognition research. In International Conference on Brain Inspired Cognitive Systems (pp. 392-402). Springer Berlin Heidelberg.
  22. Lee, H. C., Wu, C. Y., & Lin, T. M. (2013). Facial Expression Recognition Using Image Processing Techniques and Neural Networks. In Advances in Intelligent Systems and Applications-Volume 2 (pp. 259-267). Springer Berlin Heidelberg.
  23. Song, Y., Huang, J., Zhou, D., Zha, H., & Giles, C. L. (2007, September). Iknn: Informative k-nearest neighbor pattern classification. In European Conference on Principles of Data Mining and Knowledge Discovery (pp. 248-264). Springer Berlin Heidelberg.
  24. Wang, Y., Ai, H., Wu, B., & Huang, C. (2004, August). Real time facial expression recognition with adaboost. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on (Vol. 3, pp. 926-929). IEEE.
  25. Schmidt, M., Schels, M., & Schwenker, F. (2010, April). A hidden markov model based approach for facial expression recognition in image sequences. In IAPR Workshop on Artificial Neural Networks in Pattern Recognition (pp. 149-160). Springer Berlin Heidelberg.
  26. Viola, P., & Jones, M. (2001). Robust real-time object detection. International Journal of Computer Vision, 4.
  27. Wilson, P. I., & Fernandez, J. (2006). Facial feature detection using Haar classifiers. Journal of Computing Sciences in Colleges, 21(4), 127-133.
  28. Lee, T. S. (1996). Image representation using 2D Gabor wavelets. IEEE Transactions on pattern analysis and machine intelligence, 18(10), 959-971.
  29. Deng, H. B., Jin, L. W., Zhen, L. X., & Huang, J. C. (2005). A new facial expression recognition method based on local gabor filter bank and pca plus lda. International Journal of Information Technology, 11(11), 86-96.
  30. Haghighat, M., Zonouz, S., & Abdel-Mottaleb, M. (2015). CloudID: trustworthy cloud-based and cross-enterprise biometric identification. Expert Systems with Applications, 42(21), 7905-7916.
  31. Turk, M. A., & Pentland, A. P. (1991, June). Face recognition using eigen faces. In Computer Vision and Pattern Recognition, 1991. Proceedings CVPR'91., IEEE Computer Society Conference on (pp. 586-591). IEEE.
  32. Vyas, A., & Tokas, R. Fast Face Recognition Using Eigen Faces.
  33. Chen, L., Zhou, C., & Shen, L. (2012). Facial Expression Recognition Based on SVM in E-learning. IERI Procedia, 2, 781-787.
  34. Duan, K. B., & Keerthi, S. S. (2005, June). Which is the best multiclass SVM method? An empirical study. In International Workshop on Multiple Classifier Systems (pp. 278-285). Springer Berlin Heidelberg.
  35. Jottrand, M. (2005). Support Vector Machines for Classification applied to Facial Expression Analysis and Remote Sensing.
  36. Maheswari, K. S. & Babu, C. H. (2015). A Color Face Recognition Using PCA and KNN Classifier, international journal & magazine of engineering technology, management and research, vol2, issues: 9, pp.1110-1116.
  37. Ewees, A. A., Eisa, M., & Refaat, M. M. (2014). Comparison of cosine similarity and k-NN for automated essays scoring. Cognitive processing, 3(12).
  38. Suja, P., Tripathi, S., & Deepthy, J. (2014). Emotion recognition from facial expressions using frequency domain techniques. In Advances in signal processing and intelligent recognition systems (pp. 299-310). Springer International Publishing.
  39. Zhang, B., Shan, S., Chen, X., & Gao, W. (2007). Histogram of Gabor phase patterns (HGPP): a novel object representation approach for face recognition. IEEE Transactions on Image Processing, 16(1), 57-68.
  40. Abuqaaud, K. A. (2013). Face Recognition in Uncontrolled Indoor Environment (Doctoral dissertation, American University of Sharjah).
  41. Dong-liang, P., & An-ke, X. (2005, October). Degraded image enhancement with applications in robot vision. In 2005 IEEE International Conference on Systems, Man and Cybernetics (Vol. 2, pp. 1837-1842). IEEE.
  42. Liu, C., & Wechsler, H. (2002). Gabor feature based classification using the enhanced fisher linear discriminate model for face recognition. IEEE Transactions on Image processing, 11(4), 467-476.
  43. Shen, L., Bai, L., & Fairhurst, M. (2007). Gabor wavelets and general discriminant analysis for face identification and verification. Image and Vision Computing, 25(5), 553-563.
  44. Chang, C. C., & Lin, C. J. (2001). LIBSVM: A library for support vector machines, Software available at h ttp. WWW. CSIE. NTU. EDU. TW/∼ CJLIN/ PAPERS/ LIBSVM.
  45. Hsieh, C. C., Hsih, M. H., Jiang, M. K., Cheng, Y. M., & Liang, E. H. (2015). Effective semantic features for facial expressions recognition using SVM. Multimedia Tools and Applications, 1-20.
  46. Fahn, C. S., Wu, M. H., & Kao, C. Y. (2009, October). Real-time facial expression recognition in image sequences using an AdaBoost-based multi-classifier. In Proceedings: Asia-Pacific Signal and Information Processing Association, 2009 Annual Summit and Conference (pp. 8-17).
Index Terms

Computer Science
Information Sciences

Keywords

Real-time System Facial Expression Recognition Classification SVM k-NN Image Processing.