The week's pick
Reseach Article
Facial Expression Recognition using PCA Algorithm
| Communications on Applied Electronics |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 3 - Number 3 |
| Year of Publication: 2015 |
| Authors: Shweta Patil, S.S.Katariya |
10.5120/cae2015651904
|
Shweta Patil, S.S.Katariya . Facial Expression Recognition using PCA Algorithm. Communications on Applied Electronics. 3, 3 ( October 2015), 22-24. DOI=10.5120/cae2015651904
Abstract
Face is the primary focus for the identity of human. But while detecting the face one difficulty is there. How to deal with the variations in the facial expressions, lightening etc. in this paper we use the principal component analysis (PCA) algorithm for the detection of facial expression. First the eigen spaces are created with the help of eigen vectors and eigen values. With the help of this space eigen faces are created and with the help of PCA algorithm the most matching eigen face is selected. The databases of 30 persons are generated each person having 10 photographs with different expressions like happy, angry, sad, neutral etc. If any expression is not recognize then it consider as a neutral expression. The classifier used are based on Euclidian distance. Train and test databases are there but that should be in similar conditions such as distance, lightening, background etc. The results shows the accuracy of this algorithm.
References
- Abhishek Singh and Saurabh Kumar “Face Recognition using Principal component analysis and Eigen Face approach”
- “Comparative Study of Facial expression Recognition”, International Journal of Computer Applications (0975 - 8887)Volume 13- No.1, January 2011
- Ashraf Abbas M. Al-modwahi, Onkemetse Sebetela, Lefoko Nehemiah Batleng, Behrang Parhizkar, Arash Habibi Lashkari “Facial Expression Recognition Intelligent security System For real Time Surveillanc”
- “Recognition of Facial Expression with PCA and Singular value Decomposition” International Journal of Computer Applications Volume 9U No.12, November 2010.
- Seyed Mehdi Lajevardi, Zahir M. Hussai “ Local Feature Extraction method for Facial expression recognition”, 17th European Signal Processing Conference (EUSIPCO 2009) Glasgow, Scotland, August 24-28, 2009
- Bartlett, M. A., Ekman, P., Hager, J. C., and Sejnowski T.,1999, "Measuring facial expressions by computer image analysis", Journal of Psychophysiology, Vol. 36, No. 2, pp.253-263.
- Bartlett, M. S., Donato, G., Ekman, P., Hager, J. C.,Sejnowski, T.J., 1999,"Classifying Facial Actions", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 21,No. 10, pp. 974-989
- Cohn, J., Kanade, T., Lien, J., 2000, "Detection, tracking and classification of action units in facial expression",Journal of Robotics and Autonomous Systems, Vol. 31, pp.131-146.
- Jain, A.K., Duin R.P.W., Mao J., 2000,"Statistical Pattern Recognition: A Review", IEEE Trans. Pattern Analysis and Machine Intelligence, Vol. 22, No. 1,pp. 4-37.
- Pantic, M. and Rothkrantz, L., 2000, "Automatic analysis of facial expressions: The state of the art", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 12, pp.1424-1445.
- Iyengar, P.A., Samal, A., (1992) "Automatic Recognition andAnalysis of Human Faces and Facial Expressions: A Survey", Pattern Recognition, Vol. 25, No. 1, pp. 65-77.
- Chellappa, R., Sirohey S., Wilson C.L., (1995) "Human andMachine Recognition of Faces: a Survey", Proc. IEEE, Vol.83, No. 5, pp. 705-741. [5] Cohen W., Fast Effective Rule Induction, In Proc. 12th international Conf. Machine Learning (ICML’95), pp 115-123, 1995.
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