Abstract
In this paper, we propose a combination approach for face recognition based on combination of two
features extractor schemes named Singular Value Decomposition and Gabor filters. Singular value
decomposition (SVD) is a good method to extract image features because it has invariance for the
rotation and mirroring transformation, and also has better robustness for noise and light intensity
transform. Gabor filters produce perfect localization features in frequency and spatial domains. From
theexperimental results, the suggested approach obtains a good recognition rate. A recognition rate
of more than 98% has been achieved on the ORL database.The proposed approach has also been
compared to some existing techniques and the results obtained by the proposed method are far better
than these techniques.
features extractor schemes named Singular Value Decomposition and Gabor filters. Singular value
decomposition (SVD) is a good method to extract image features because it has invariance for the
rotation and mirroring transformation, and also has better robustness for noise and light intensity
transform. Gabor filters produce perfect localization features in frequency and spatial domains. From
theexperimental results, the suggested approach obtains a good recognition rate. A recognition rate
of more than 98% has been achieved on the ORL database.The proposed approach has also been
compared to some existing techniques and the results obtained by the proposed method are far better
than these techniques.
Keywords
Face Recognition
Gabor filters
Pattern Recognition
Singular Value Decomposition (SVD)