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PCA-Based Face Recognition: Similarity Measures and Number of Eigenvectors

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Image Analysis and Recognition (ICIAR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9730))

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Abstract

This paper examines the performance of face recognition using Principal Component Analysis by (i) varying number of eigenvectors; and (ii) using different similarity measures for classification. We tested 15 similarity measures. ORL database is used for experimentation work which consists of 400 face images. We observed that changing similarity measure causes significant change in the performance. System showed best performance using following distance measures: Cosine, Correlation and City block. Using Cosine similarity measure, we needed to extract lesser images (30 %) in order to achieve cumulative recognition of 100 %. The performance of the system improved with the increasing number of eigenvectors (till roughly 30 % of eigenvectors). After that performance almost stabilized. Some of the worst performers are Standardized Euclidean, Weighted Modified SSE and Weighted Modified Manhattan.

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References

  1. Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face recognition: a literature survey. ACM Comput. Surv. 35, 399–458 (2003)

    Article  Google Scholar 

  2. Jafri, R., Arabnia, H.R.: A survey of face recognition techniques. J. Inf. Process. Syst. 5, 41–68 (2009)

    Article  Google Scholar 

  3. Rao, A., Noushath, S.: Subspace methods for face recognition. Comput. Sci. Rev. 4, 1–17 (2010)

    Article  Google Scholar 

  4. Sirovich, L., Kirby, M.: Low-dimensional procedure for the characterization of human faces. J. Opt. Soc. Am. 4, 519–524 (1987)

    Article  Google Scholar 

  5. Turk, M., Pentland, A.P.: Eigenfaces for recognition. J. Cogn. Neurosci. 3, 71–86 (1991)

    Article  Google Scholar 

  6. Yambor, W., Draper, B.: Analyzing PCA-based face recognition algorithm: eigenvector selection and distance measures. In: Christensen, H., Phillips, J. (eds.) Empirical Evaluation Methods in Computer Vision, pp. 39–51. World Scientific Press, Singapore (2002)

    Chapter  Google Scholar 

  7. Mathwork Help for Similarity Measures. http://www.mathworks.com/help/stats/pdist2.html

  8. Perlibakas, V.: Distance measures for PCA-based face recognition. Pattern Recogn. Lett. 25, 711–724 (2004)

    Article  Google Scholar 

  9. Miller, P., Lyle, J.: The effect of distance measures on the recognition rates of PCA and LDA based facial recognition. In: Digital Image Processing. Clemson University, Clemson

    Google Scholar 

  10. Moon, H., Phillips, P.J.: Computational and performance aspects of PCA-based face recognition algorithms. Perception 30, 303–321 (2001)

    Article  Google Scholar 

  11. Borade, S.N., Deshmukh, R.R.: Effect of distance measures on the performance of face recognition using principal component analysis. In: Berretti, S. (ed.) Intelligent Systems Technologies and Applications, AISC, vol. 384, pp. 569–577. Springer, Heidelberg (2016)

    Chapter  Google Scholar 

  12. Biometrics Testing and Statistics. www.biometrics.gov/documnets/biotestingandstats.pdf

  13. ORL Face Database. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, 431004, India

    Sushma Niket Borade & Ratnadeep R. Deshmukh

Authors
  1. Sushma Niket Borade
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  2. Ratnadeep R. Deshmukh
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Correspondence to Sushma Niket Borade .

Editor information

Editors and Affiliations

  1. University of Porto, Porto, Portugal

    Aurélio Campilho

  2. Department of Electrical, University of Waterloo, Waterloo, Ontario, Canada

    Fakhri Karray

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Borade, S.N., Deshmukh, R.R. (2016). PCA-Based Face Recognition: Similarity Measures and Number of Eigenvectors. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_9

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  • DOI: https://doi.org/10.1007/978-3-319-41501-7_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-41500-0

  • Online ISBN: 978-3-319-41501-7

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