TY - GEN
T1 - Efficient multi-algorithmic approaches for face recognition using subspace methods
AU - Imran, Mohammad
AU - Noushath, S.
AU - Abdesselam, Abdelhamid
AU - Jetly, Karan
AU - Karthikeyan,
PY - 2013
Y1 - 2013
N2 - Face recognition is an active research in the field of biometrics due to its potential benefits to various security based applications. To make the results of face recognition unsusceptible to different kinds of variations in the image and to enhance the accuracy, robustness of two or more methods can be fused in a single framework. The fusion can be achieved at various levels. Objective of this paper is to suggest optimal fusion of subspace methods to achieve robust results for various test conditions. This is achieved by performing feature level fusion of popular subspace methods namely PCA, LDA, LPP and ICA1. The Fusion is performed by considering different combinations of set of two, three and four subspace methods. Experiments are conducted by using two different databases: ORL and Yale. Experimental results suggest that by the fusion of these subspace approaches; there is a significant improvement in the accuracy compared to performance of an individual subspace method. This work helped us to determine the optimal combination of subspace methods to achieve robust results for specific test conditions.
AB - Face recognition is an active research in the field of biometrics due to its potential benefits to various security based applications. To make the results of face recognition unsusceptible to different kinds of variations in the image and to enhance the accuracy, robustness of two or more methods can be fused in a single framework. The fusion can be achieved at various levels. Objective of this paper is to suggest optimal fusion of subspace methods to achieve robust results for various test conditions. This is achieved by performing feature level fusion of popular subspace methods namely PCA, LDA, LPP and ICA1. The Fusion is performed by considering different combinations of set of two, three and four subspace methods. Experiments are conducted by using two different databases: ORL and Yale. Experimental results suggest that by the fusion of these subspace approaches; there is a significant improvement in the accuracy compared to performance of an individual subspace method. This work helped us to determine the optimal combination of subspace methods to achieve robust results for specific test conditions.
KW - Biometrics
KW - Face
KW - Feature level
KW - Fusion
KW - subspace methods
UR - http://www.scopus.com/inward/record.url?scp=84876040648&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84876040648&partnerID=8YFLogxK
U2 - 10.1109/ICCSPA.2013.6487264
DO - 10.1109/ICCSPA.2013.6487264
M3 - Conference contribution
AN - SCOPUS:84876040648
SN - 9781467328210
T3 - 2013 1st International Conference on Communications, Signal Processing and Their Applications, ICCSPA 2013
BT - 2013 1st International Conference on Communications, Signal Processing and Their Applications, ICCSPA 2013
T2 - 2013 1st International Conference on Communications, Signal Processing and Their Applications, ICCSPA 2013
Y2 - 12 February 2013 through 14 February 2013
ER -