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Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 150))

Abstract

The objective of the paper is face recognition using PCA and Bit plane slicing. It made a study on the dimensionality reduction on bit plane of images for face recognition. The proposed frame work would aid in robust design of face recognition system and addressed the challenging issues like pose and expression variation on ORL face database. It is in contrast to PCA on the image the design of PCA on bit plane reduces computation complexity and also reduces time. In the proposed frame work image is decomposed with the help of bit plane slicing, the feature have been extracted from the principle component analysis (PCA).

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References

  1. Patil AM, Kolhe SR, Patil PM (2009) Face recognition by PCA technique. In: Proceedings of the second international conference on emerging trends in engineering and technology, ICETET 2009, pp 192–195

    Google Scholar 

  2. Kokiopoulou E, Saad Y (2004) PCA and kernel PCA using polynomial filtering: a case study on face recognition

    Google Scholar 

  3. Meedeniya DA, Ratnaweera DAAC (2007) Enhanced face recognition through variation of principle component analysis (PCA). In: Second international conference on industrial and information systems, ICIIS 2007, pp 347–352

    Google Scholar 

  4. Kwang KI, Jung K, Kim HJ (2002) Face recognition using kernel principal component analysis. IEEE Signal Process Lett 9(2):40–42

    Google Scholar 

  5. Poon1 B, Ashraful Amin2 M, Yan H (2009) PCA based face recognition and testing criteria. In: Proceedings of the eighth international conference on machine learning and cybernetics, pp 2945–2949

    Google Scholar 

  6. AT&T Laboratories Cambridge, The Database of Faces, formerly ORL face database. Available at www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

  7. Sirovich L, Kirby M (1987) A low dimensional procedure for characterization of human faces. J Optical Soc Am A 4(3) 519–524

    Google Scholar 

  8. Punnam Chandar K, Mahesh Chandra M, Raman Kumar M, Swarna Latha B (2011) Preprocessing using SVD towards illumination invariant face recognition. In: Proceedings of IEEE RAICS 2011

    Google Scholar 

  9. Wu SQ, Wei LZ, Fang ZJ, Li RW, Ye XQ (2007) Infrared face recognition based on blood perfusion and sub-block DCT in wavelet domain. In: International conference on wavelet analysis and pattern recognition, vol 3, p 1252

    Google Scholar 

  10. Turk M, Pentland A (1991) Eigen faces for recognition. J Cognit Neurosci 3(1):71–86

    Article  Google Scholar 

  11. Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces versus fisherfaces: recognition using class specific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  12. Bartlett MS, Movellan JR, Sejnowski TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Netw 13(6):1450–1464

    Article  Google Scholar 

  13. Moghaddam B, Nastar C, Pentland A (1996) A Bayesian similarity measure for direct measure for direct image matching. Proc Int Conf Pattern Recognit 2:350–358

    Article  Google Scholar 

  14. Moghaddam B, Wahid W, Pentland A (1998) Beyond eigenfaces: Probabilistic matching for face recognition. In Proceedings of IEEE international conference on automatic face and gesture recognition, pp 30–35

    Google Scholar 

  15. Lanitis A, Taylor CJ, Cootes TF (1995) Automatic face identification system using flexible appearance models. Image Vis Comput 13(5):393–401

    Article  Google Scholar 

  16. Shashua A, Riklin-Raviv T (2001) The quotient image: class-based rerendering and recognition with varying illuminations. IEEE Trans Pattern Anal Mach Intell 23(2):129–139

    Article  Google Scholar 

  17. Zhang L, Samaras D (2003) Face recognition under variable lighting using harmonic image exemplars. In Proceedings of IEEE conference computer vision and pattern recognition, vol 1, pp 19–25

    Google Scholar 

  18. Chien J-T, Wu C–C (2002) Discriminant wavelet faces and nearest feature classifiers for face recognition. IEEE Trans Pattern Anal Mach Intell 24(12):1644–1649

    Article  Google Scholar 

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Correspondence to T. Srinivas .

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Srinivas, T., Sandeep Mohan, P., Shiva Shankar, R., Surender Reddy, C., Naganjaneyulu, P.V. (2013). Face Recognition Using PCA and Bit-Plane Slicing. In: Das, V. (eds) Proceedings of the Third International Conference on Trends in Information, Telecommunication and Computing. Lecture Notes in Electrical Engineering, vol 150. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3363-7_60

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  • DOI: https://doi.org/10.1007/978-1-4614-3363-7_60

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