Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30184
Non-negative Principal Component Analysis for Face Recognition

Authors: Zhang Yan, Yu Bin

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)

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1082457

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1350

References:


[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.