@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},