International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 10, No. 9, 8 Sep. 2018
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Pattern Averaging Technique for Facial Expression Recognition Using Support Vector Machines
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Author(s)
Index Terms
Facial expression recognition, pattern averaging, support vector machine, human-computer interaction
Abstract
Facial expression is one of the nonverbal communication methods of identifying an emotional state of a human being. Due to its crucial importance in Human-Robot interaction, facial expression recognition (FER) is in the limelight of recent research activities. Most of the studies consider the whole expression images in their analysis, and it has several has several drawbacks concerning illumination, orientation, texture, zoom level, time and space complexity. In this paper, a novel feature extraction technique called the pattern averaging is studied on whole image data using reduction in the dimension of the image by averaging the neighboring pixels. The study is found to give better results on standard datasets using support vector machine classifier.
Cite This Paper
N. P. Gopalan, Sivaiah Bellamkonda, " Pattern Averaging Technique for Facial Expression Recognition Using Support Vector Machines", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.10, No.9, pp. 27-33, 2018. DOI: 10.5815/ijigsp.2018.09.04
Reference
[1]P. Ekman, and W. Friesen, “Facial Action Coding System: A Technique for the Measurement of Facial Movements”, Consulting Psychologists Press, California, 1978.
[2]Mehrabian.A, “Communication without Words”, Psychology Today, 1968. Vo1.2, No.4, pp 53-56.
[3]Yanpeng Liu, Yuwen Cao, Yibin Li, Ming Liu, Rui Song, “Facial Expression Recognition with PCA and LBP Features Extracting from Active Facial Patches”, IEEE International Conference on Real-time Computing and Robotics June 6-9, 2016, Angkor Wat, Cambodia, pp. 368-341, 2016.
[4]Parth Patel, Khushali Raval, “Facial Expression Recognition Using DWT-PCA with SVM Classifier”, International Journal for Scientific Research & Development, Vol. 3, Issue 03, pp. 1531-1537, 2015.
[5]Yuan Luo, Cai-ming Wu, Yi Zhang, “Facial expression recognition based on fusion feature of PCA and LBP with SVM”, Optik - International Journal for Light and Electron Optics Volume 124, Issue 17, pp. 2767-2770, 2013.
[6]Muzammil Abdurrahman, Alaa Eleyan, “Facial expression recognition using Support Vector Machines”, 23rd IEEE Conference on Signal Processing and Communications Applications Conference (SIU), 16-19 May 2015, Malatya, Turkey, 2015.
[7]Anushree Basu, Aurobinda Routray, Suprosanna Shit, Alok Kanti Deb, “Human emotion recognition from facial thermal image based on fused statistical feature and multi-class SVM”, 2015 Annual IEEE India Conference (INDICON), 17-20 Dec. 2015, New Delhi, India, 2015.
[8]Mahesh Kumbhar, Manasi Patil, Ashish Jadhav, “Facial Expression Recognition using Gabor Wavelet”, International Journal of Computer Applications, Volume 68, No.23, PP. 13-18, 2013.
[9]Muzammil Abdulrahman, Tajuddeen R. Gwadabe, Fahad J. Abdu, Alaa Eleyan, “Gabor Wavelet Transform Based Facial Expression Recognition Using PCA and LBP”, IEEE 22nd Signal Processing and Communications Applications Conference (SIU 2014), Trabzon, Turkey, 23-25 April 2014, pp. 2265 - 2268, 2014.
[10]Shilpa Sharma, Kumud Sachdeva, “Face Recognition using PCA and SVM with Surf Technique”, International Journal of Computer Applications, Volume 129, No.4, pp. 41-47, 2015.
[11]Vinay A., Vinay S. Shekhar, K. N. Balasubramanya Murthy, S. Natarajan, “Face Recognition using Gabor Wavelet Features with PCA and KPCA - A Comparative Study, 3rd International Conference on Recent Trends in Computing 2015 (ICRTC-2015), Procedia Computer Science 57, pp. 650–659, 2015.
[12]Anurag De, Ashim Sahaa, Dr. M.C Pal, “A Human Facial Expression Recognition Model based on Eigen Face Approach”, International Conference on Advanced Computing Technologies and Applications (ICACTA-2015), pp. 282-289, 2015.
[13]Liangke Gui, Tadas Baltrusaitis, and Louis-Philippe Morency, “Curriculum Learning for Facial Expression Recognition”, 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), Washington, DC, USA, 2017
[14]Zhiming Su, Jingying Chen, Haiqing Chen, “Dynamic facial expression recognition using autoregressive models”, 7th International Congress on Image and Signal Processing (CISP), 14-16 Oct. 2014, Dalian, China, 2014.
[15]Mao Xu, Wei Cheng, Qian Zhao, Li Ma, Fang Xu, “Facial Expression Recognition based on Transfer Learning from Deep Convolutional Networks”, 11th IEEE International Conference on Natural Computation (ICNC), 15-17 Aug. 2015, Zhangjiajie, China, 2015.
[16]Pan Z., Polceanu M., Lisetti C., “On Constrained Local Model Feature Normalization for Facial Expression Recognition”, in Traum D., Swartout W., Khooshabeh P., Kopp S., Scherer S., Leuski A. (eds) Intelligent Virtual Agents. IVA 2016, Lecture Notes in Computer Science, Vol. 10011. Springer, Cham, 2016.
[17]Shaoping Zhu, “Pain Expression Recognition Based on pLSA Model”, The Scientific World Journal, Volume 2014, 2014.
[18]Myunghoon Suk and Balakrishnan Prabhakaran, “Real-time Mobile Facial Expression Recognition System – A Case Study”, 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 23-28 June 2014, Columbus, OH, USA, 2014.
[19]Zineb Elgarrai, Othmane El Meslouhi, Mustapha Kardouchi, Hakim Allali, “Robust facial expression recognition system based on hidden Markov models”, International Journal of Multimedia Information Retrieval, Volume 5, Issue 4, pp. 229–236, 2016.
[20]Guo Y., Zhao G., Pietikäinen M., “Dynamic Facial Expression Recognition Using Longitudinal Facial Expression Atlases”, In Fitzgibbon A., Lazebnik S., Perona P., Sato Y., Schmid C. (eds) Computer Vision – ECCV 2012, Lecture Notes in Computer Science, vol. 7573. Springer, Berlin, Heidelberg, 2012.
[21]M. Z. Uddin, M. M. Hassan, A. Almogren, A. Alamri, M. Alrubaian and G. Fortino, “Facial Expression Recognition Utilizing Local Direction-Based Robust Features and Deep Belief Network,” in IEEE Access, vol. 5, pp. 4525-4536, 2017.
[22]S. Xie and H. Hu, “Facial expression recognition with FRR-CNN,” in Electronics Letters, vol. 53, no. 4, pp. 235-237, 2 16 2017.
[23]Tsai, HH. & Chang, YC., “Facial expression recognition using a combination of multiple facial features and support vector machine”, Soft Comput (2017).
[24]Kanade, T., Cohn, J. F., Tian, Y., “Comprehensive database for facial expression analysis”, Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition (FG'00), Grenoble, France, pp. 46-53, 2010.
[25]Michael J. Lyons, Shigeru Akamatsu, Miyuki Kamachi, Jiro Gyoba, “Coding Facial Expressions with Gabor Wavelets”, Proceedings of the third IEEE International Conference on Automatic Face and Gesture Recognition, Nara Japan, IEEE Computer Society, pp. 200-205, 1998.
[26]M. Pantic, M.F. Valstar, R. Rademaker and L. Maat, “Webbased database for facial expression analysis”, Proceedings of IEEE International Conference on Multimedia and Expo (ICME'05), Amsterdam, The Netherlands, July 2005.