Commenced in January 2007
Paper Count: 30184
Rotation Invariant Face Recognition Based on Hybrid LPT/DCT Features
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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1077876Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1505
 W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, "Face Recognition: A Literature Survey," ACM Computing Surveys, vol. 35, no. 4, pp. 399-458, 2003.
 R. Brunelli and T. Poggio, "Face Recognition: Features versus Templates," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 10, pp. 1042-1052, 1993.
 M. A. Turk and A. P. Pentland, "Face Recognition using Eigenfaces," Proc. of IEEE Conference on Computer Vision and Pattern Recognition, pp. 586-591, June 1991.
 X. Yi-qiong, L. Bi-cheng and W. Bo, "Face Recognition by Fast Independent Component Analysis and Genetic Algorithm," Proc. of the 4th International Conference on Computer and Information Technology (CIT-04), pp. 194-198, Sept. 2004.
 K. Nakamura, and S. Miyamoto, "Rotation, size and shape recognition by a spreading associative neural network," IEICE Trance. on Information and Systems, vol.E-84-D, no.8, pp.1075-1084, 2001.
 K. Nakamura, K. Arimura, and T. Yoshikawa, "Recognition of Object Orientation and Shape by a Rotation Spreading Associative Neural Network," Proc. IEEE-INNS Int. Joint Conf. on Neural Networks (JCNN2001), pp.565-570, 2001.
 H. El-Bakry, "A Rotation Invariant Algorithm for Recognition," Fuzzy Days 2001, LNCS 2206, pp. 284-290, 2001. Springer-Verlag Berlin Heidelberg 2001.
 H. R. Wilson, D. Levi, L. Maffei, J. Rovamo, and R. DeValois, "The Perception of Form: Retina to Striate Cortex", Visual Perception: The Neurophisiologcal Foundations, Academic Press, 1990.
 S. Chien, and I. Choi, "Face and Facial Landmarks location based onLog-Polar Mapping", Lecture Notes in Computer Science - LNCS 1811, pp. 379-386, 2000.
 S. Minut, S. Mahadevan, J. Henderson, and F. Dyer, "Face Recognition using Foveal Vision", Lecture Notes in Computer Science - LNCS 1811, pp. 424-433, 2000.
 M. Tistarelli, and E. Grosso, "Active Vision-Based Face Authentication", Image and Vision Computing, no. 18, pp. 299-314, 2000.
 A. S. Samra, S. E. Gad Allah, R. M. Ibrahim, "Face Recognition Using Wavelet Transform, Fast Fourier Transform and Discrete Cosine Transform," Proc. 46th IEEE International Midwest Symp. Circuits and Systems (MWSCAS'03), vol. 1, pp. 272- 275, 2003.
 Z. Yankun and L. Chongqing, "Efficient Face Recognition Method based on DCT and LDA," Journal of Systems Engineering and Electronics, vol. 15, no. 2, pp. 211-216, 2004.
 Z. M. Hafed and M. D. Levine, "Face Recognition Using Discrete Cosine Transform, " International Journal of Computer Vision, vol. 43, no. 3, pp. 167-188. 2001.
 F. M. Matos, L. V. Batista, and J. Poel, "Face Recognition Using DCT Coefficients Selection," Proc. of the 2008 ACM Symposium on Applied Computing, (SAC-08),pp. 1753-1757, March 2008.
 M. Yu, G. Yan, and Q.-W. Zhu, "New Face Recognition Method Based on DWT/DCT Combined Feature Selection," Proc. 5th International Conference on Machine Learning and Cybernetics, pp. 3233-3236, August 2006.
 K. Hyun Kim, Y.-S. Chung, J.-H. Yoo, and Y. Man Ro, "Facial Feature Extraction Based on Private Energy Map in DCT Domain," ETRI Journal, Volume 29, Number 2, pp. 243-245, April 2007
 E. Kokiopoulou and P. Frossard, "Classification-Specific Feature Sampling for Face Recognition," Proc. IEEE 8th Workshop on Multimedia Signal Processing, pp. 20-23, 2006.
 X. Fan and B. Verma, "Face recognition: A New Feature Selection and Classification Technique," Proc. 7th Asia-Pacific Conference on Complex Systems, December 2004.
 A. Y. Yang, J. Wright,Y. Ma, and S. S. Sastry, " Feature Selection in Face Recognition: A Sparse Representation Perspective," submitted for publication, 2007.
 R. M. Ramadan, and R. F. Abdel-Kader, "Face Recognition Using Particle Swarm Optimization-Based Selected Features," In Press, 2008.
 J. Kennedy and R. Eberhart, "Particle swarm optimization," Proc. IEEE International Conference on Neural Networks, pp. 1942-1948, 1995.
 J. Kennedy and R. C. Eberhart, "A Discrete Binary Version of the Particle Swarm Algorithm", Proc. IEEE International Conference on Systems, Man, and Cybernetics, vol. 5, pp. 4104-4108, Oct. 1997.