Abstract
In this paper a new hybrid scheme for overcoming variations in facial images based on the score fusion strategy is considered. The scheme takes into account Log-Gabor transform to extract facial features. The implemented scheme applies Backtracking Search Algorithm (BSA) as a novel feature selection method and Linear Discriminant Analysis (LDA) as a feature transformation method to reduce the number of features and computational cost. Then Weighted Sum Rule (WS) fusion technique is applied to fuse the produced scores for our face recognition system. The robustness of schemes is tested using FERET and ORL database. Experimental results show a significant improvement of proposed scheme over implemented methods in this study.
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Yildiz, M.C., Sharifi, O., Eskandari, M. (2016). Log-Gabor Transforms and Score Fusion to Overcome Variations in Appearance for Face Recognition. In: Chmielewski, L., Datta, A., Kozera, R., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2016. Lecture Notes in Computer Science(), vol 9972. Springer, Cham. https://doi.org/10.1007/978-3-319-46418-3_31
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