{"title":"Rotation Invariant Face Recognition Based on Hybrid LPT\/DCT Features","authors":"Rehab F. Abdel-Kader, Rabab M. Ramadan, Rawya Y. Rizk","volume":20,"journal":"International Journal of Electrical and Computer Engineering","pagesStart":1613,"pagesEnd":1619,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/11753","abstract":"The recognition of human faces, especially those with\r\ndifferent orientations is a challenging and important problem in image\r\nanalysis and classification. This paper proposes an effective scheme\r\nfor rotation invariant face recognition using Log-Polar Transform and\r\nDiscrete Cosine Transform combined features. The rotation invariant\r\nfeature extraction for a given face image involves applying the logpolar\r\ntransform to eliminate the rotation effect and to produce a row\r\nshifted log-polar image. The discrete cosine transform is then applied\r\nto eliminate the row shift effect and to generate the low-dimensional\r\nfeature vector. A PSO-based feature selection algorithm is utilized to\r\nsearch the feature vector space for the optimal feature subset.\r\nEvolution is driven by a fitness function defined in terms of\r\nmaximizing the between-class separation (scatter index).\r\nExperimental results, based on the ORL face database using testing\r\ndata sets for images with different orientations; show that the\r\nproposed system outperforms other face recognition methods. The\r\noverall recognition rate for the rotated test images being 97%,\r\ndemonstrating that the extracted feature vector is an effective rotation\r\ninvariant feature set with minimal set of selected features.","references":"[1] W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, \"Face\r\nRecognition: A Literature Survey,\" ACM Computing Surveys, vol. 35,\r\nno. 4, pp. 399-458, 2003.\r\n[2] R. 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