PATTERN RECOGNITION FROM FACE IMAGES

Abstract

In this article, we use projected gradient descent nonnegative matrix factorization (NMF-PGD) method and make pattern recognition analysis on ORL face data set. Face recognition is one of the critical issues in our life and some security, daily activities and operations use this well known application area. NMF-PGD is a type of nonnegative matrix factorization (NMF) which defined in the literature. In the study, derived NMF-PGD definition and algorithm has been used in order to classify the ORL face images. We give the experimental results in a table and graph. According to experiments, face recognition accuracy rates have different accuracy values because of the k - lower rank value. We change k-values between 25 and 144 to see the performance of NMF-PGD. At the end, we make some analysis and comments on the recognition rates. Additionally, NMF-PGD can also be used for different kind of pattern recognition problems.

Keywords

• Pattern Recognition,Classification,Face Recognition,Nonnegative Matrix Factorization

References

1. [1] X. Li, J. Zhou, L. Tong, X. Yu, J. Guo, C. Zhao, Structured Discriminative Nonnegative Matrix Factorization for Hyperspectral Unmixing, IEEE International Conference on Image Processing, (2016), pp. 1848-1852.
2. [2] T. Ensari, J. Chorowski, and J. M. Zurada, “Correntropy-based Document Clustering via Nonnegative Matrix Factorization”, in Proc. Int. Conf. on Artificial Neural Networks, vol. 7553, (2012), pp. 347-354.
3. [3] T. Ensari, Character Recognition Analysis with Nonnegative Matrix Factorization, International Journal of Computers, vol. 1, (2016), pp. 219-222.
4. [4] C. Fevotte and J. Idier, “Algorithms for Nonnegative Matrix Factorization with the â-Divergence”, Neural Computation, vol. 23, no. 9, (2011), pp. 2421-2456.
5. [5] S. Choi, “Algorithms for orthogonal nonnegative matrix factorization”, in Proc. Int. Joint Conf. on Neural Networks, Hong Kong, (2008), pp. 1828-1832.
6. 6] D. D. Lee and S. Seung, Learning the Parts of Objects by Nonnegative Matrix Factorization, Nature 401, (1999), pp. 788-791. [7] D. D. Lee and S. Seung, (2000), Algorithms for Nonnegative Matrix Factorization, International Conference on Neural Information Processing.
7. [8] Alpaydın E.: Introduction to Machine Learning, The MIT Press, USA, (2015).
8. [9] M. Hu, Y. Zheng, F. Ren, H. Jiang: Age estimation and gender classification of facial images based on local directional pattern, IEEE International Conf. on Cloud Computing and Intelligent Systems, (2014), 103-107.

9. [10] Wiesner V., Evers L.: Statistical data mining, University of Oxford, (2004).
10. [11] T. Bissoon, S. Viriri: Gender classification using Face Recognition, International Conference on Adaptive Science and Technology, (2013), 1-4.
11. [12] W. Zhao, H. Ma, and N. Li, “A new non-negative matrix factorization algorithm with sparseness constraints”, in Proc. the Int. Conf. on Machine Learning and Cybernetics, (2011), pp 1449-1452.
12. [13] H. Liu, Z. Wu, X. Li, D. Cai, and T. S. Huang, “Constrained nonnegative matrix factorization for image representation”, IEEE Trans. on Pattern Analysis and Machine Intelligence, (2012), vol. 34, no. 7.
13. [14] R. Gopalan, D. Jacobs, “Comparing and Combining Lighting Insensitive Approaches for Face Recognition”, Journal of Computer Vision and Image Understanding, (2010), pp 135-145, vol. 114, no. 1.
14. [15] S. Zafeiriou, A. Tefas, I. Buci, and I. Pitas, “Exploiting Discriminant Information in Nonnegative Matrix Factorization with Application to Frontal Face Verification”, IEEE Transactions on Neural Networks, (2006), vol. 17, no. 3.
15. [16] D. Guillamet, J. Vitria, “Nonnegative Matrix Factorization for Face Recognition”, Proc. of Catalonian Conference on Artificial Intelligence, (2002).
16. [17] T. Feng, S. Z. Li, H. Y. Shum, H. J. Zhang, “Local Non-negative Matrix Factorization as A Visual Representation”, Proc. of Int. Conf. on Development and Learning, (2002).