The week's pick
Random Articles
Reseach Article
An approach to Illumination and Expression Invariant Multiple Classifier Face Recognition
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 91 - Number 15 |
| Year of Publication: 2014 |
| Authors: Hidangmayum Saxena Devi, Dalton Meitei Thounaojam, Romesh Laishram |
10.5120/15959-5335
|
Hidangmayum Saxena Devi, Dalton Meitei Thounaojam, Romesh Laishram . An approach to Illumination and Expression Invariant Multiple Classifier Face Recognition. International Journal of Computer Applications. 91, 15 ( April 2014), 34-37. DOI=10.5120/15959-5335
Abstract
Face recognition is still a challenging task due to the different problems such as pose, illumination, expression and occlusions. Due to these problems, it needs an extensive research. Various face recognition techniques exists but they suffer from one or the other limitations. One technique could not provide robust solution. Therefore, a combination of different face classification techniques can lead to robust solution. Here, the individual output of three different classifiers PCA, KPCA and Fisher face are normalized and combined using SUM rule, with their individual normalized matching score as the input feature vector. The system is tested on AT&T Face Database in which our proposed system is robust against illumination and expression as well as increasing the recognition performance.
References
- Turk, Matthew, and Pentland A. 1997 Eigenfaces for recognition. Journal of cognitive neuroscience. pp. 71-86.
- Kim K. 1996 Face recognition using Principle Component Analysis. International Conference on Computer Vision and Pattern Recognition. pp. 586-591.
- Moon H. and Phillips P. J, 2001 Computational and performance aspects of PCA-based face-recognition algorithms. Perception-London, pp. 303-322.
- Scholkopf B, Smola A. and Muller K. R. 1996 Nonlinear component analysis as a kernel eigenvalue problem. Technical report on Neural computation.
- Ebied, H. M. 2012 Feature extraction using PCA and Kernel-PCA for face recognition. In International Conference on Informatics and Systems.
- Ebied, Hala M. 2012. Kernel-PCA for face recognition in different color spaces. In International Conference on Computer Engineering & Systems.
- Belhumeur, Peter N. , Joao Hespanha P, and David K. 1997. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence pp. 711-720.
- Chelali F. Z. , Djeradi A and Djeradi R. 2009. Linear discriminant analysis for face recognition. International Conference on Multimedia Computing and Systems, IEEE. pp. 1-10.
- Kittler, Josef, Mohamad H, Robert PW Duin, and Jiri M. 1998. On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence. pp. 226-239.
- Lu, X, Wang Y, and Anil K. Jain. 2003 Combining classifiers for face recognition. Proceedings of IEEE on Multimedia and Expo.
- Huang G. B, Ramesh M, Berg T, Learned-Miller E. 2007 Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. Technical Report on University of Massachusetts, Amherst.
- Štruc, Vitomir, and Pavensic N. 2009 Gabor-based kernel partial-least-squares discrimination features for face recognition. Informatica.
- Štruc, Vitomir, and Paveši? N. 2010 The complete Gabor-fisher classifier for robust face recognition. EURASIP Journal on Advances in Signal Processing.
- Sanderson C, Lovell B. C. 2009 Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference.
- Sadeghi, Mohammad T, Masoumeh S, and Kittler J. 2010 Fusion of PCA-based and LDA-based similarity measures for face verification. EURASIP Journal on Advances in Signal Processing.
Index Terms
Keywords