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Face Recognition Using Non-negative Matrix Factorization (NMF) An Analysis of Order of Decomposition on Recognition Rate

Author Affiliations

  • 1 Department of Computer Science, Federal Urdu University of Arts, Sciences and Technology, Karachi, PAKISTAN

Res. J. Recent Sci., Volume 4, Issue (4), Pages 77-82, April,2 (2015)

Abstract

Non-negative Matrix Factorization (NMF) is a well established dimension reduction technique. NMF reduces the dimension of the non-negative data matrix by applying the non- negativity constraint. One of the applications of NMF is recognition problem (e-g face recognition). To solve recognition problem using NMF, the order of decomposition play an important role on the recognition rate. This paper presents the details analysis of the order of decomposition on the recognition rate. A standard face dataset A and T is used for analysis.

References

  1. Torres L., Is there any hope for face recognition?, In Proc. of the 5th International Workshop on Image Analysis for Multimedia Interactive Services, WIAMIS, Lisboa, Portugal, 21-23, (2004)
  2. Gross R., Shi J. and Cohn J., Quo vadis face recognition?, The current state of the art in face recognition, Technical report, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA, (2001)
  3. Guo G., Li S. and Chan K., Face recognition by support vector machines, In Proc. of the IEEE International Conference on Automatic Face and Gesture Recognition, 196–201, Grenoble, France, (2000)
  4. Moghaddam and Pentland A., Probabilistic visual learning for object representation, IEEE Transactions on Pattern Analysis and Machine Intelligence,19(7), 696–710 (1997)
  5. Belhumeur P., Hespanha J. and Kriegman D., Eigenfaces vs. fisherfaces: Recognition using class specific linear projection, IEEE Transactions on Pattern Analysis and Machine Intelligence,19(7), 711–720 (1997)
  6. Bartlett M., Movellan J. and Sejnowski T., Face recognition by independent component analysis, IEEE Trans. on Neural Networks,13(6), 1450–1464 (2002)
  7. Brunelli R and Poggio T., Face recognition: Features versus templates, IEEE Transactions on Pattern Analysis and Machine Intelligence,15(10), 1042–1052 (1993)
  8. Nixon M., Eye spacing measurement for facial recognition, Proceedings of the Society of Photo-Optical Instrument Engineers, SPIE,575(37), 279–285 (1985)
  9. Wiskott L., Fellous J.M., Krueuger N and von der Malsburg C., Face recognition by elastic bunch graph matching, IEEE Trans on Pattern Analysis and Machine Intelligence,19(7), 775–779 (1997)
  10. Lee D.D. and Seung H.S., Learning the parts of objects by non-negative matrix factorization, Nature, 401, 788-791 (1999)
  11. Muhammad Sharif S.M.M.R., Shah J.H., Sub-holistic hidden markov model for face recognition, Res. J. of Rec. Sci.,2(5), 10–14 (2013)
  12. Ahsraf M., Sarim M., Shaikh A.B., Raffat S.K. and Siddiq M., Face recognition using weighted distance transform, Res. J. of Rec. Sci., (2013)