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Abstract
Face recognition is that the powerful task from the pictures that is done by a machine that is ready to determine the face of a human being. Automatic face recognition is that the still awfully acknowledged and hard to please stuffed of the image methodology thanks to that the researchers are taking the interest in it. Each face has its own characteristics and specification like size of the eyes position of the nose over the face and in addition the facial expression corresponding to Lips position and motion of lips over the face. The Face recognition has become a very dynamic space of research in recent years in the main thanks to upward security demands and its potential profitable and enforcement applications. The last decade has shown spectacular progress throughout this space, with emphasis on such applications as human-computer interface (HCI), biometric investigation, and content-based secret writing of metaphors and videos, and supervision. Feature based mostly facial expression for face recognition continues to be an extremely hot and hard task and since that we tend to our plan to propose an honest technique for it with a high performance rate towards these methods comparatively.
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How to Cite
Rathore, M., & Satsangi, C. S. (2015). Facial Expression Based Face Recognition Using Machine Learning with SVM Kernel: HMM and FDA. International Journal of Emerging Trends in Science and Technology, 2(03). Retrieved from https://igmpublication.org/ijetst.in/index.php/ijetst/article/view/525
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2. J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
3. I. S. Jacobs and C. P. Bean, “Fine particles, thin films and exchange anisotropy,†in Magnetism, vol. III, G. T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271–350.
4. K. Elissa, “Title of paper if known,†unpublished.
5. R. Nicole, “Title of paper with only first word capitalized,†J. Name Stand. Abbrev., in press.
6. Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, “Electron spectroscopy studies on magneto-optical media and plastic substrate interface,†IEEE Transl. J. Magn. Japan, vol. 2, pp. 740–741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982].
7. M. Young, The Technical Writer's Handbook. Mill Valley, CA: University Science, 198 Jawad Nagi, Syed Khaleel Ahmed and Farrukh Nagi, “A MATLAB based Face Recognition System using Image Processing and Neural Networksâ€, in 4th International Colloquium on Signal Processing and its Applications, March 7-9, 2008, Kuala Lumpur, Malaysia.
8. Yongzhong Lu, Jingli Zhou and Shengsheng Yu, “A Survey of Face Detection, Extraction and Recognitionâ€, Computing and Informatics, Vol. 22, 2003.
9. S Venkatesh, S Palanivel and B Yegnanarayana, “Face Detection and Recognition in an Image Sequence using Eigenedginessâ€,
10. Li, P., Phung, S., Bouzerdoum, A. & Tivive, F., “Feature selection for facial expression recognitionâ€, in 2nd European Workshop on Visual Information Processing (pp. 35-39). USA: IEEE. (2010)
11. Rabia Jafri and Hamid R. Arabnia, “A Survey of Face Recognition Techniquesâ€, in Journal of Information Processing Systems, Vol.5, No.2, June 2009.
12. Michel Valstar and Maja Pantic,â€Fully Automatic Facial Action Unit Detection and Temporal Analysisâ€,in Conference on Computer Vision and Pattern Recognition Workshop, 2006 IEEE.
13. Shuai-Shi Liu, Yan-Tao Tian And Dong Li,“New Research Advances Of Facial Expression Recognitionâ€,in Eighth International Conference on Machine Learning and Cybernetics, Baoding, 12-15 July 2009.
14. Jun Ou，Xiao-Bo Bai，Yun Pei, Liang Ma and Wei Liu,“Automatic Facial Expression Recognition Using Gabor Filter And Expression Analysisâ€, in Second International Conference on Computer Modeling and Simulation, 2010 IEEE.
15. R A Patil, Vineet Sahula and A. S. Mandal,“Automatic Recognition of Facial Expressions in Image Sequences: A Reviewâ€,in 5th International Conference on Industrial and Information Systems, ICIIS 2010, Jul 29 - Aug 01, 2010 IEEE, India.
16. Kai-Tai Song and Yi-Wen Chen,“A Design for Integrated Face and Facial Expression Recognitionâ€,In 2011 IEEE.
17. Nazil Perveen, Shubhrata Gupta, and Kesari Verma,“Facial Expression Recognition Using Facial Characteristic Points and Gini Indexâ€,in 2012 IEEE.
18. Kaimin Yu, Zhiyong Wang, Genliang Guan, Qiuxia Wu, Zheru Chi , and Dagan Feng,“How many frames does facial expression recognition require?â€,in International Conference on Multimedia and Expo Workshops, 2012 IEEE.