@article{(Open Science Index):https://publications.waset.org/pdf/11753, title = {Rotation Invariant Face Recognition Based on Hybrid LPT/DCT Features}, author = {Rehab F. Abdel-Kader and Rabab M. Ramadan and Rawya Y. Rizk}, country = {}, institution = {}, abstract = {The recognition of human faces, especially those with different orientations is a challenging and important problem in image analysis and classification. This paper proposes an effective scheme for rotation invariant face recognition using Log-Polar Transform and Discrete Cosine Transform combined features. The rotation invariant feature extraction for a given face image involves applying the logpolar transform to eliminate the rotation effect and to produce a row shifted log-polar image. The discrete cosine transform is then applied to eliminate the row shift effect and to generate the low-dimensional feature vector. A PSO-based feature selection algorithm is utilized to search the feature vector space for the optimal feature subset. Evolution is driven by a fitness function defined in terms of maximizing the between-class separation (scatter index). Experimental results, based on the ORL face database using testing data sets for images with different orientations; show that the proposed system outperforms other face recognition methods. The overall recognition rate for the rotated test images being 97%, demonstrating that the extracted feature vector is an effective rotation invariant feature set with minimal set of selected features.}, journal = {International Journal of Electrical and Computer Engineering}, volume = {2}, number = {8}, year = {2008}, pages = {1613 - 1618}, ee = {https://publications.waset.org/pdf/11753}, url = {https://publications.waset.org/vol/20}, bibsource = {https://publications.waset.org/}, issn = {eISSN: 1307-6892}, publisher = {World Academy of Science, Engineering and Technology}, index = {Open Science Index 20, 2008}, }