The week's pick
Reseach Article
Real Time Facial Emotion Recognition based on Image Processing and Machine Learning
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 139 - Number 11 |
| Year of Publication: 2016 |
| Authors: Rituparna Halder, Sushmit Sengupta, Arnab Pal, Sudipta Ghosh, Debashish Kundu |
10.5120/ijca2016908707
|
Rituparna Halder, Sushmit Sengupta, Arnab Pal, Sudipta Ghosh, Debashish Kundu . Real Time Facial Emotion Recognition based on Image Processing and Machine Learning. International Journal of Computer Applications. 139, 11 ( April 2016), 16-19. DOI=10.5120/ijca2016908707
Abstract
Behaviors, actions, poses, facial expressions and speech; these are considered as channels that convey human emotions. Extensive research has being carried out to explore the relationships between these channels and emotions. This paper proposes a prototype system which automatically recognizes the emotion represented on a face. Thus a neural network based solution combined with image processing is used in classifying the universal emotions: Happiness, Sadness, Anger, Disgust, Surprise and Fear. Colored frontal face images are given as input to the prototype system. After the face is detected, image processing based feature point extraction method is used to extract a set of selected feature points. Finally, a set of values obtained after processing those extracted feature points are given as input to the neural network to recognize the emotion contained.
References
- Bhuiyan, M. A.-A., Ampornaramveth, V., Muto, S., and Ueno, H., Face detection and facial feature localization for human-machine interface. NII Journal, (5):25–39, 2003.
- Brunelli, R., and Poggio., T., Face recognition: features versus templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(10):1042–1052, 1993.
- Busso, C., Deng, Z., Yildirim, S., Bulut, M., Lee, C. M., Kazemzadeh, A., Lee, S., Neumann, U., and Narayanan, S., Analysis of emotion recognition using facial expressions, speech and multimodal information. In ICMI ’04: Proceedings of the 6th international conference on Multimodal interfaces, pages 205–211, New York, NY, USA, 2004. ACM.
- Cohen, I., Garg, A., and Huang, T. S., Emotion recognition from facial expressions using multilevel hmm. In Neural Information Processing Systems, 2000.
- Dailey, M. N., Cottrell, G. W., Padgett, C., and Adolphs, R., Empath: A neural network that categorizes facial expressions. J. Cognitive Neuroscience, 14(8):1158–1173, 2002.
- Deng, X., Chang, C.-H., and Brandle, E., A new method for eye extraction from facial image. In DELTA ’04: Proceedings of the Second IEEE International Workshop on Electronic Design, Test and Applications, page 29, Washington, DC, USA, 2004. IEEE Computer Society
- Dumasm, M., Emotional expression recognition using support vector machines. Technical report, Machine Perception Lab, Univeristy of California, 2001.
- Ekman, P., Facial expression and emotion. American Psychologist, 48:384–392, 1993.
- Grimm, M., Dastidar, D. G., and Kroschel, K., Recognizing emotions in spontaneous facial expressions. 2008.
- Gu, H., Su, G., and Du, C., Feature points extraction from faces. 2003. 11) Lisetti, C. L., and Rumelhart, D. E., Facial expression recognition using a neural network. In Proceedings of the Eleveth International FLAIRS Conference. Menlo Park, pages 328–332. AAAI Press, 1998.
- Nilsson, M., Nordberg, J., and Claesson, I., Face detection using local smqt features and split up snow classifier. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2007.
- Pantic, M., Rothkrantz, L. J. M., and Koppelaar, H., Automation of nonverbal communication of facial expressions. In: EUROMEDIA 98, SCS International, pages 86–93, 1998. 14) Pham, T., and Worring, M., Face detection methods: A critical evaluation. ISIS Technical Report Series, University of Amsterdam, 11, 2000.
- Pham, T. V., Worring, M., and Smeulders, A. W. M., Face detection by aggregated Bayesian network classifiers. Pattern Recogn. Lett., 23(4):451–461, 2002.
- Sanderson C., and Paliwal, K. K., Fast feature extraction method for robust face verification. Electronics Letters, 8:1648 – 1650, 2002. 17) Sebe, N., Sun, Y., Bakker, E., Lew, M. S., Cohen, I., and Huang, T. S., Towards authentic emotion recognition. 18) Te,o W. K., Silva, L. C. D., and Vadakkepat, P., Facial expression detection and recognition system. 2008.
- Wallhoff, F., Facial expressions and emotion database, ttp://www.mmk.ei.tum.de/waf/fgnet/ feedtum.html, last accessed date: 01st june 2009, 2006.
- Yang, M. H., Ahuja, N., and Kriegman, D., A survey on face detection methods, 1999.
- Yang, M. H., D. Kriegman, J., Member, S., and Ahuja, N., Detecting faces in images: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24:34–58, 2002.
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