Non-negative Principal Component Analysis for Face Recognition
Principle component analysis is often combined with
the state-of-art classification algorithms to recognize human faces.
However, principle component analysis can only capture these
features contributing to the global characteristics of data because it is a
global feature selection algorithm. It misses those features
contributing to the local characteristics of data because each principal
component only contains some levels of global characteristics of data.
In this study, we present a novel face recognition approach using
non-negative principal component analysis which is added with the
constraint of non-negative to improve data locality and contribute to
elucidating latent data structures. Experiments are performed on the
Cambridge ORL face database. We demonstrate the strong
performances of the algorithm in recognizing human faces in
comparison with PCA and NREMF approaches.
[1] A. K. Jain, R. Bolle, and S. Pankanti, Biometrics: Personal Identification
in Networked Security, Kluwer Academic Publishers, 1999.
[2] K. Kim, "Intelligent Immigration Control System by Using International
Symposium on Neural Networks," in Proc. The International Symposium
on Neural Networks, Chongqing, 2005, pp. 147-156.
[3] J. N. K. Liu, M. Wang, and B. Feng, "iBotGuard: an Internet-based
intelligent robot security system using invariant face recognition against
intruder," IEEE Transactions on Systems Man and Cybernetics Part
C-Applications and Reviews, vol. 35, pp. 97-105, 2005.
[4] H. Moon, "Biometrics Person Authentication Using Projection-Based
Face Recognition System in Verification Scenario," in Proc. Of
International Conference on Bioinformatics and its Applications, Hong
Kong, 2004, pp. 207-213.
[5] K. Balci, V. Atalay, "PCA for Gender Estimation: Which Eigenvectors
Contribute?," in Proc. Of 16th International Conference on Pattern
Recognition, vol. 3, Qubec, 2002, pp. 363-366.
[6] B. Moghaddam, M. H. Yang, "Learning Gender with Support Faces,"
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24,
pp. 707-711, 2002.
[7] R. Brunelli, T. Poggio, "HyperBF Networks for Gender Classification,"
in Proc. Of DARPA Image Understanding Workshop, 1992, pp.
311-314.
[8] A. Colmenarez, B. J. Frey, T. S. Huang, "A Probabilistic framework for
embedded face and facial expression recognition," in Proc. Of the IEEE
Conference on Computer Vision and Pattern Recognition, vol. 1, Ft.
Collins, CO, 1999, pp. 1592-1597.
[9] Y. Shinohara, N. Otsu, "Facial Expression Recognition Using Fisher
Weight Maps," in Proc. Of 16th IEEE International Conference on
Automatic Face and Gesture Recognition, vol. 100, 2004, pp. 499-504.
[10] F. Bourel, C. C. Chibelushi, A. A. Low, "Robust Facial Feature
Tracking," in Proc. Of British Machine Vision Conference, Bristol, 2000,
pp. 232-241.
[11] F. Galton, "Personal Identification and Description," Nature, pp. 173-177,
June 21, 1888.
[12] R. Brunelli, T. Poggio, "Face Recognition: features versus templates,"
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15,
1993, pp. 1042-1052.
[13] M. A. Grudin, "On Internal Representations in Face Recognition
Systems," Pattern Recognition, vol. 33, pp. 1161-1177, 2000.
[14] B. Heisele, P. Ho, J, Wu, and T. Poggio, "Face Recognition:
Component-based versus global approaches," Computer Vision and
Image Understanding, vol. 91, pp. 6-21, 2003.
[15] L. Wiskott, J. M. Fellous, N. Kruger, and C. von der Malsburg, "Face
Recognition by Elastic Bunch Graph Matching," IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. 19, 1997, pp. 775-77.
[16] H. Shin, S. D. Kim, and H.C. Choi, "Generalized Elastic Graph Matching
for Face Recognition," Pattern Recognition Letters, vol. 28, pp.
1077-1082, 2007.
[17] A. Albiol, D. Monzo, A. Martin, J. Sastre, "Face Recognition using
HOG-EBGM," Pattern Recognition Letters, vol. 29, pp. 1537-1543,
2008.
[18] R. Cendrillon and B. C. Lowel, "Real-time Face Recognition using
Eigenfaces," in Proceedings of the SPIE International Conference on
Visual Communications and Image Processing, vol. 4067, 2000, pp.
269-276.
[19] M. A. O. Vasilescu, D. Terzopoulos, "Multilinear Subspace Analysis of
Image Ensemblers," in Proceedings of IEEE International Conference on
Computer Vision and Pattern Recognition, 2003, pp. 93-99.
[20] Q. Yang, X. Q. Ding, "Symmetrical Principal Component Analysis and
Its Application in Face Recognition," Chinese Journal of Computers, vol.
26, pp. 1146-1151, 2003.
[21] J. Meng, W. Zhang, "Volume measure in 2DPCA-based Face
Recognition," Pattern Recognition Letters, vol. 28, pp. 1203-1208, 2007.
[22] A. P. Kumer, S. Das, V. Kamakoti, "Face Recognition using weighted
modular principle component analysis," Neural Information Processing,
vol. 3316, Lecture Notes In Computer Science: Springer Berlin /
Heidelberg, 2004, pp. 362-367.
[23] N. Sun, H. Wang, Z. Ji, C. Zou, L. Zhao, "An Efficient algorithm for
Kernel Two-dimensional Principal Component Analysis," Neural
Computing & Applications, vol. 17, pp. 59-64, 2008.
[24] D. Zhang, Z. Zhoua, S. Chen, "Diagonal Principal Component Analysis
for Face Recognition," Pattern Recognition, vol. 39, pp. 140-142, 2006.
[25] D. Lee, H. Seung, "Learning the Parts of Objects by Non-Negative Matrix
Factorization," Nature, vol. 401, pp. 1788-1791, 1999.
[26] X. Han, "Nonnegative Principal Component Analysis for Cancer
Molecular Pattern Discovery," IEEE/ACM Transactions on
Computational Biology and Bioinformatics, vol. 7, pp. 537-549, 2010.
[27] P. Hoyer, "Non-Negative Matrix Factorization with Sparseness
Constrains," J. Machine Learning Research, vol. 5, pp. 1457-1469, 2004.
[28] A. Dempster, N. Laird, D. Rubin, "Maximum likelihood from incomplete
data via the EM algorithm," Royal statistical Society B, Vol. 39, pp. 1-38,
1977.
[29] D. D. Lee and H. S. Seung, "Learning The Parts of Objects by
Nonnegative Matrix Factorization," Nature, vol. 401, pp. 788-791, 1999.
[30] D. D. Lee and H. S. Seung, "Algorithms for Nonnegative Matrix
Factorization," in Proceedings of Neural Information Processing
Systems, 2000, pp. 556-562.
[31] C. Li et al., "Major Copy Proportion Analysis of Tumor Samples Using
SNP Arrays," BMC Bioinformatics, vol. 9, no. 204, 2008, doi:
10.1186/1471-2105-9-20.
[32] J. Liu, S. Ranka, and T. Kahveci, "Classification and Feature Selection
Algorithms for Multi-Class CGH Data," Bioinformatics, vol. 24, pp.
186-195, 2008.
[33] M. Plumbley and E. Oja, "A ÔÇÿNonnegative PCA- Algorithm for
Independent Component Analysis," IEEE Trans. Neural Networks, vol.
15, pp. 66-76, Jan. 2004.
[34] M. Plumbley, "Algorithms for Nonnegative Independent Component
Analysis," IEEE Trans. Neural Networks, vol. 4, pp. 534-543, May 2003.
[35] F. Bach and M. Jordan, "Kernel Independent Component Analysis," J.
Machine Learning and Research, vol. 3, pp. 1-48, July 2002.
[36] C. Ding, T. Li, and M. Jordan, "Convex and Semi-Nonnegative Matrix
Factorizations," IEEE Trans. Pattern Analysis and Machine Intelligence,
vol. 32, pp. 45-55, Jan. 2010.
[1] A. K. Jain, R. Bolle, and S. Pankanti, Biometrics: Personal Identification
in Networked Security, Kluwer Academic Publishers, 1999.
[2] K. Kim, "Intelligent Immigration Control System by Using International
Symposium on Neural Networks," in Proc. The International Symposium
on Neural Networks, Chongqing, 2005, pp. 147-156.
[3] J. N. K. Liu, M. Wang, and B. Feng, "iBotGuard: an Internet-based
intelligent robot security system using invariant face recognition against
intruder," IEEE Transactions on Systems Man and Cybernetics Part
C-Applications and Reviews, vol. 35, pp. 97-105, 2005.
[4] H. Moon, "Biometrics Person Authentication Using Projection-Based
Face Recognition System in Verification Scenario," in Proc. Of
International Conference on Bioinformatics and its Applications, Hong
Kong, 2004, pp. 207-213.
[5] K. Balci, V. Atalay, "PCA for Gender Estimation: Which Eigenvectors
Contribute?," in Proc. Of 16th International Conference on Pattern
Recognition, vol. 3, Qubec, 2002, pp. 363-366.
[6] B. Moghaddam, M. H. Yang, "Learning Gender with Support Faces,"
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24,
pp. 707-711, 2002.
[7] R. Brunelli, T. Poggio, "HyperBF Networks for Gender Classification,"
in Proc. Of DARPA Image Understanding Workshop, 1992, pp.
311-314.
[8] A. Colmenarez, B. J. Frey, T. S. Huang, "A Probabilistic framework for
embedded face and facial expression recognition," in Proc. Of the IEEE
Conference on Computer Vision and Pattern Recognition, vol. 1, Ft.
Collins, CO, 1999, pp. 1592-1597.
[9] Y. Shinohara, N. Otsu, "Facial Expression Recognition Using Fisher
Weight Maps," in Proc. Of 16th IEEE International Conference on
Automatic Face and Gesture Recognition, vol. 100, 2004, pp. 499-504.
[10] F. Bourel, C. C. Chibelushi, A. A. Low, "Robust Facial Feature
Tracking," in Proc. Of British Machine Vision Conference, Bristol, 2000,
pp. 232-241.
[11] F. Galton, "Personal Identification and Description," Nature, pp. 173-177,
June 21, 1888.
[12] R. Brunelli, T. Poggio, "Face Recognition: features versus templates,"
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15,
1993, pp. 1042-1052.
[13] M. A. Grudin, "On Internal Representations in Face Recognition
Systems," Pattern Recognition, vol. 33, pp. 1161-1177, 2000.
[14] B. Heisele, P. Ho, J, Wu, and T. Poggio, "Face Recognition:
Component-based versus global approaches," Computer Vision and
Image Understanding, vol. 91, pp. 6-21, 2003.
[15] L. Wiskott, J. M. Fellous, N. Kruger, and C. von der Malsburg, "Face
Recognition by Elastic Bunch Graph Matching," IEEE Transactions on
Pattern Analysis and Machine Intelligence, vol. 19, 1997, pp. 775-77.
[16] H. Shin, S. D. Kim, and H.C. Choi, "Generalized Elastic Graph Matching
for Face Recognition," Pattern Recognition Letters, vol. 28, pp.
1077-1082, 2007.
[17] A. Albiol, D. Monzo, A. Martin, J. Sastre, "Face Recognition using
HOG-EBGM," Pattern Recognition Letters, vol. 29, pp. 1537-1543,
2008.
[18] R. Cendrillon and B. C. Lowel, "Real-time Face Recognition using
Eigenfaces," in Proceedings of the SPIE International Conference on
Visual Communications and Image Processing, vol. 4067, 2000, pp.
269-276.
[19] M. A. O. Vasilescu, D. Terzopoulos, "Multilinear Subspace Analysis of
Image Ensemblers," in Proceedings of IEEE International Conference on
Computer Vision and Pattern Recognition, 2003, pp. 93-99.
[20] Q. Yang, X. Q. Ding, "Symmetrical Principal Component Analysis and
Its Application in Face Recognition," Chinese Journal of Computers, vol.
26, pp. 1146-1151, 2003.
[21] J. Meng, W. Zhang, "Volume measure in 2DPCA-based Face
Recognition," Pattern Recognition Letters, vol. 28, pp. 1203-1208, 2007.
[22] A. P. Kumer, S. Das, V. Kamakoti, "Face Recognition using weighted
modular principle component analysis," Neural Information Processing,
vol. 3316, Lecture Notes In Computer Science: Springer Berlin /
Heidelberg, 2004, pp. 362-367.
[23] N. Sun, H. Wang, Z. Ji, C. Zou, L. Zhao, "An Efficient algorithm for
Kernel Two-dimensional Principal Component Analysis," Neural
Computing & Applications, vol. 17, pp. 59-64, 2008.
[24] D. Zhang, Z. Zhoua, S. Chen, "Diagonal Principal Component Analysis
for Face Recognition," Pattern Recognition, vol. 39, pp. 140-142, 2006.
[25] D. Lee, H. Seung, "Learning the Parts of Objects by Non-Negative Matrix
Factorization," Nature, vol. 401, pp. 1788-1791, 1999.
[26] X. Han, "Nonnegative Principal Component Analysis for Cancer
Molecular Pattern Discovery," IEEE/ACM Transactions on
Computational Biology and Bioinformatics, vol. 7, pp. 537-549, 2010.
[27] P. Hoyer, "Non-Negative Matrix Factorization with Sparseness
Constrains," J. Machine Learning Research, vol. 5, pp. 1457-1469, 2004.
[28] A. Dempster, N. Laird, D. Rubin, "Maximum likelihood from incomplete
data via the EM algorithm," Royal statistical Society B, Vol. 39, pp. 1-38,
1977.
[29] D. D. Lee and H. S. Seung, "Learning The Parts of Objects by
Nonnegative Matrix Factorization," Nature, vol. 401, pp. 788-791, 1999.
[30] D. D. Lee and H. S. Seung, "Algorithms for Nonnegative Matrix
Factorization," in Proceedings of Neural Information Processing
Systems, 2000, pp. 556-562.
[31] C. Li et al., "Major Copy Proportion Analysis of Tumor Samples Using
SNP Arrays," BMC Bioinformatics, vol. 9, no. 204, 2008, doi:
10.1186/1471-2105-9-20.
[32] J. Liu, S. Ranka, and T. Kahveci, "Classification and Feature Selection
Algorithms for Multi-Class CGH Data," Bioinformatics, vol. 24, pp.
186-195, 2008.
[33] M. Plumbley and E. Oja, "A ÔÇÿNonnegative PCA- Algorithm for
Independent Component Analysis," IEEE Trans. Neural Networks, vol.
15, pp. 66-76, Jan. 2004.
[34] M. Plumbley, "Algorithms for Nonnegative Independent Component
Analysis," IEEE Trans. Neural Networks, vol. 4, pp. 534-543, May 2003.
[35] F. Bach and M. Jordan, "Kernel Independent Component Analysis," J.
Machine Learning and Research, vol. 3, pp. 1-48, July 2002.
[36] C. Ding, T. Li, and M. Jordan, "Convex and Semi-Nonnegative Matrix
Factorizations," IEEE Trans. Pattern Analysis and Machine Intelligence,
vol. 32, pp. 45-55, Jan. 2010.
@article{"International Journal of Electrical, Electronic and Communication Sciences:63160", author = "Zhang Yan and Yu Bin", title = "Non-negative Principal Component Analysis for Face Recognition", abstract = "Principle component analysis is often combined with
the state-of-art classification algorithms to recognize human faces.
However, principle component analysis can only capture these
features contributing to the global characteristics of data because it is a
global feature selection algorithm. It misses those features
contributing to the local characteristics of data because each principal
component only contains some levels of global characteristics of data.
In this study, we present a novel face recognition approach using
non-negative principal component analysis which is added with the
constraint of non-negative to improve data locality and contribute to
elucidating latent data structures. Experiments are performed on the
Cambridge ORL face database. We demonstrate the strong
performances of the algorithm in recognizing human faces in
comparison with PCA and NREMF approaches.", keywords = "classification, face recognition, non-negativeprinciple component analysis (NPCA)", volume = "4", number = "12", pages = "1870-5", }
