Rotation Invariant Face Recognition Based on Hybrid LPT/DCT Features
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.
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] H. El-Bakry, "A Rotation Invariant Algorithm for Recognition," Fuzzy
Days 2001, LNCS 2206, pp. 284-290, 2001. Springer-Verlag Berlin
Heidelberg 2001.
[8] 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.
[9] 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.
[10] 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.
[11] M. Tistarelli, and E. Grosso, "Active Vision-Based Face
Authentication", Image and Vision Computing, no. 18, pp. 299-314,
2000.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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
[18] E. Kokiopoulou and P. Frossard, "Classification-Specific Feature
Sampling for Face Recognition," Proc. IEEE 8th Workshop on
Multimedia Signal Processing, pp. 20-23, 2006.
[19] X. Fan and B. Verma, "Face recognition: A New Feature Selection and
Classification Technique," Proc. 7th Asia-Pacific Conference on
Complex Systems, December 2004.
[20] A. Y. Yang, J. Wright,Y. Ma, and S. S. Sastry, " Feature Selection in
Face Recognition: A Sparse Representation Perspective," submitted for
publication, 2007.
[21] R. M. Ramadan, and R. F. Abdel-Kader, "Face Recognition Using
Particle Swarm Optimization-Based Selected Features," In Press, 2008.
[22] J. Kennedy and R. Eberhart, "Particle swarm optimization," Proc. IEEE
International Conference on Neural Networks, pp. 1942-1948, 1995.
[23] 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.
[1] 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.
[2] 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.
[3] 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.
[4] 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.
[5] 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.
[6] 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.
[7] H. El-Bakry, "A Rotation Invariant Algorithm for Recognition," Fuzzy
Days 2001, LNCS 2206, pp. 284-290, 2001. Springer-Verlag Berlin
Heidelberg 2001.
[8] 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.
[9] 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.
[10] 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.
[11] M. Tistarelli, and E. Grosso, "Active Vision-Based Face
Authentication", Image and Vision Computing, no. 18, pp. 299-314,
2000.
[12] 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.
[13] 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.
[14] 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.
[15] 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.
[16] 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.
[17] 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
[18] E. Kokiopoulou and P. Frossard, "Classification-Specific Feature
Sampling for Face Recognition," Proc. IEEE 8th Workshop on
Multimedia Signal Processing, pp. 20-23, 2006.
[19] X. Fan and B. Verma, "Face recognition: A New Feature Selection and
Classification Technique," Proc. 7th Asia-Pacific Conference on
Complex Systems, December 2004.
[20] A. Y. Yang, J. Wright,Y. Ma, and S. S. Sastry, " Feature Selection in
Face Recognition: A Sparse Representation Perspective," submitted for
publication, 2007.
[21] R. M. Ramadan, and R. F. Abdel-Kader, "Face Recognition Using
Particle Swarm Optimization-Based Selected Features," In Press, 2008.
[22] J. Kennedy and R. Eberhart, "Particle swarm optimization," Proc. IEEE
International Conference on Neural Networks, pp. 1942-1948, 1995.
[23] 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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:60786", author = "Rehab F. Abdel-Kader and Rabab M. Ramadan and Rawya Y. Rizk", title = "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.", keywords = "Discrete Cosine Transform, Face Recognition,Feature Extraction, Log Polar Transform, Particle SwarmOptimization.", volume = "2", number = "8", pages = "1693-6", }
