Browse > Article
http://dx.doi.org/10.5573/ieie.2017.54.3.55

Face Recognition using Non-negative Matrix Factorization and Learning Vector Quantization  

Jin, Donghan (Dept. of Information and Telecommunication Eng., Incheon National University)
Kang, Hyunchul (Dept. of Information and Telecommunication Eng., Incheon National University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.54, no.3, 2017 , pp. 55-62 More about this Journal
Abstract
Non-negative matrix factorization (NMF) is one of the typical parts-based representation in which images are expressed as a linear combination of basis vectors that show the lcoal features or objects in the images. In this paper, we represent face images using various NMF methods and recognize their face identities based on extracted features using a learning vector quantization. We analyzed the various NMF methods by comparing extracted basis vectors. Also we confirmed the availability of NMF to the face recognition by verification of recognition rate of the various NMF methods.
Keywords
face recognition; NMF; parts-based representation; local feature; learning vector quantization;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 A. Samil and P. Iyengar, "Automatic Recognition and Analysis of Human Face and Facial Expression: A Survey," Pattern Recognition, vol. 25, pp. 65-77, 1992.   DOI
2 R. Chellappa, C. Wilson, and S. Sirohey, "Human and Machine Recognition of Face: A Survey," Proc. of IEEE, vol. 83, no. 5, pp. 705-740, 1995.   DOI
3 A. Pentland, "Looking at People : Sensing for Ubiquitous and Wearable Computing," IEEE Trans. PAMI, vol. 22, no. 1, pp. 107-119, Jan. 2000.   DOI
4 M. H. Yang, D. J. Kriegman, and N. Ahuja, "Detecting Faces in Image: A Survey," IEEE Trans. PAMI, vol. 24, no. 1, pp. 34-58, Jan. 2002.   DOI
5 A. Pentland and M. Turk, "Eigenfaces for recognition," Journal of Cognitive Neuroscience, vol. 3, pp. 71-86, 1993.
6 Mika, S. et al. "Fisher Discriminant Analysis with Kernels", IEEE Conf. on Neural Networks for Signal Processing IX: 41-48, 1999.
7 P. Comon and C. Jutten, Handbook of Blind Source Separation, Independent Component Analysis and Applications, Academic Press, Oxford UK, 2010.
8 Y. C. Cho and S. Choi, "Nonnegative features of spectro-temporal sounds for classification," Pattern Recognition Letters, vol. 26, no. 9, pp. 1327-1336, 2005.   DOI
9 Jae Min Ban, Byeong Rae Lee and Hyunchul Kang, "Moving Vehicle Recognition using NMF in Urban Scene," Journal of Korean Institute of Communication and Information Sciences, Vol. 37C, No. 7, pp.554-564, 2012. 7.
10 Jae Min Ban and Hyunchul Kang, "Vehicle Recognition using Non-negative Tensor Factorization," Journal of the Institute of Electronics Engineers of Korea, Vol. 52, No. 5, pp.136-146, 2015. 5.
11 S. Z. Li, X. W. Hou, H. J. Zhang and Q. S. Cheng, "Learning spatially localized part-based representation," IEEE Int. Conf. on Computer Vision Pattern Recognition, Kauai, Hawaii, pp. 207-212, 2001.
12 D. D. Lee, H. S. Seung, "Algorithms for Non-Negative Matrix Factorization," Advances in Neural Information Processing Systems, vol. 13, pp. 556-562. 2001.
13 Donghan Jin and Hyunchul Kang, "Face Recognition using Non-negative Matrix Factorization," Korean Institute of Information Scientists and Engineers, 2016 Workshop on Image Processing and Image Understanding, Jeju, 2016. 2
14 D. D. Lee, H. S. Seung, "Learning the Parts of Objects by Non-Negative Matrix Factorization," Nature, vol. 401, no. 6755, pp. 788-791, 1999.   DOI
15 S. Agarwal, A. Awan, Roth, "Learning to detect objects in images via a sparse, part-based representation," IEEE Trans. PAMI, vol. 26, no. 11, pp. 1475-1490, 2004.   DOI
16 P. Hoyer, "Non-negative Matrix Factorization with Sparseness Constraints," Journal of Machine Learning Research, vol. 5, pp. 1457-1469, 2004.
17 C. Lin, "Projected Gradient Methods for Nonnegative Matrix Factorization," Neural Computation, vol. 19, no. 10, pp. 2756-2779, 2007.   DOI
18 J. H, Yoo and S. J. Choi,, "Orthogonal nonnegative matrix factorization: Multiplicative updates on stiefel manifolds," In Fyfe, C., Kim, D., Lee, S.-Y., Yin, H. (eds.) IDEAL 2008. LNCS, vol. 5326, pp. 140-147. 2008.
19 C. Ding, T. Li, W. Peng, and H. Park, "Orthogonal nonnegative matrix tri-factorizations for clustering," Proc. of the ACM SIGKDD, Philadelphia, 2006.
20 A. Edelman, T. Arias, and S. T. Smith, "The geometry of algorithms with orthogonality constraints," SIAM Journal of Matrix Analysis. Application., vol. 20, no. 2, pp. 303-353, 1998.   DOI
21 T. Kohonen, J. Hunninen, J. Kangas, J. Kaaaksonen, and K. Torkkola, "LVQ_Pak : The Learning Vector Quantization Program Package," Technical Report A30, Helsinki Univ. 1996.
22 D. Donoho and V. Stodden, "When does non-negative matrix factorization give a correct decomposition into parts?," Advances in Neural Information Processing Systems 16, MIT Press, 2003.