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Emotion Detection from Facial Expression using Support Vector Machine
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 167 - Number 8 |
| Year of Publication: 2017 |
| Authors: Vanita Jain, Pratiksha Aggarwal, Tarun Kumar, Vaibhav Taneja |
10.5120/ijca2017914398
|
Vanita Jain, Pratiksha Aggarwal, Tarun Kumar, Vaibhav Taneja . Emotion Detection from Facial Expression using Support Vector Machine. International Journal of Computer Applications. 167, 8 ( Jun 2017), 25-28. DOI=10.5120/ijca2017914398
Abstract
The objective of this paper is to apply Support Vector Machine to the problem of classifying emotion on images of human faces. This well defined problem is complicated by the natural variation in people’s faces, requiring the classification algorithm to distinguish the small number of relevant features from the large pool of input features. Three different kernels i.e., linear kernel, polynomial kernel and RBF kernel are used to recognise eight facial expressions, anger, contempt, disgust, fear, happiness, neutral, sadness and surprise of human beings in still images. Accuracy of the three kernels is compared to judge the best kernel for facial expression recognition.
References
- K. R. Kulkarni and S. B. Bagal, "Facial Expression Recognition," 2015 Annual IEEE India Conference (INDICON), New Delhi, 2015, pp. 1-5.
- Ekman, P. (1982) Emotion in the Human Face. Cambridge: Cambridge University Press.
- Paul Ekman. Pictures of facial affect. 1976.
- Emotional Expression Recognition using Support Vector Machine Melanie Dumas Department of Computer Science University of California, San Diego La Jolla, CA 92193-0114.
- Vanita Jain, Renu Taneja, Anshul Goel, Megha Kumari, "Facial Features and Biomedical Signs: A Criterion for Pain Expression Recognition", INDIACom-2017; IEEE Conference, pp- 4482-4488, 2017
- Facial Expression Recognition Using Support Vector Machine Philipp Michel and Rana El Kaliouby Computer Laboratory University of Cambridge Cambridge CB3 0FD, U.K.
- Van Gent, P. (2016). Emotion Recognition Using Facial Landmarks, Python, DLib and OpenCV. A tech blog about fun things with Python and embedded electronics.
- GUO Lei,WANG Qiu-guang, "Research of Face Detection Based on Adaboost Algorithm and OpenCV Implementation", Journal of Harbin University of Science and Technology, 2009.
- Davis E. King, "Dlib-ml: A Machine Learning Toolkit", Journal of Machine Learning Research 10 (2009) 1755-1758.
- Christian Bauckhage, "NumPy / SciPy Recipes for Data Science: Ordinary Least Squares Optimization", DOI: 10.13140/2.1.3370.3209/1
- Matthieu Perrot, Édouard Duchesnay, “Scikit-learn: Machine Learning in Python”, Journal of Machine Learning Research, 2825-2830, 2011.
- S. Zhou, X. Liang and C. Zhu, "Exploiting LCSVC Algorithm for Expression Recognition," 2008 The 9th International Conference for Young Computer Scientists, Hunan, 2008, pp. 1760-1765. doi: 10.1109/ICYCS.2008.68
- Cohn-Kanade (CK and CK+) database Download Site http://www.consortium.ri.cmu.edu/ckagree/
- Vanita Jain, Shikha Rastogi, Tannishk Sharma, Aarushi Jain, "An empirical study of Classification Algorithms for the diagnosis of Hepatitis", INDIACom-2017; IEEE Conference, pp 4477-4481, 2017
- M. Abdulrahman and A. Eleyan, "Facial expression recognition using Support Vector Machine," 2015 23nd Signal Processing and Communications Applications Conference (SIU), Malatya, 2015, pp. 276-279.
- Liyuan Chen, Changjun Zhou, Liping Shen, “Facial Expression Recognition Based on SVM in E-learning”, IERI Procedia, Volume 2, 2012, Pages 781-787.
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