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An Advanced Approach of Face Alignment for Gender Recognition Using PCA

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 236)

Abstract

In this paper we have used principal component analysis (PCA) tool by adding mathematical rigor to provide explicit solution for gender recognition by extracting feature vector. We will implement face recognition system using PCA algorithm along with the application of kernel support vector machine for error minimization. In addition by using face-rec database. This is an Eigen face approach motivated by information theory using an images database of 545 images of male and female for improved efficiency. sometimes PCA mixes data points which lead to classification error. We are improving principal component analysis (PCA) by taking vector corresponding to \(k\) minimum error unlike conventional PCA.

Keywords

Gender Recognition Principal component analysis Eigen faces SVM Euclidian distance 

References

  1. 1.
    Jain, A. K. Ross, A. Prabhakar, S.: An introduction to biometric recognition. IEEE Trans. Circ. Syst. Video Technol. 14(1), 4–20 (2004)Google Scholar
  2. 2.
    Yan, S., Xu, D., Zhang, B., Zhang, H.J.: Graph embedding and extensions: a general framework for dimensionality reduction. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 40–51 (2007)Google Scholar
  3. 3.
    Mohammed, A.A., Minhas, R., Jonathan Wu, Q.M., Sid-Ahmed, M.A.: Evaluation of face recognition technique using PCA, wavelets and SVM. Pattern Recognit. (Elsevier) 44(10–11), 6404–6408 (2011)Google Scholar
  4. 4.
    Zhao, D., Liu, Z., Xiao, R., Tang, X.: Linear laplacian discrimination for feature extraction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2007)Google Scholar
  5. 5.
    Dagher, I., Nachar, R.: Face recognition using IPCA-ICA algorithm?. IEEE Trans. Pattern Anal. Mach. Intell. 28, 996–1000 (2006)Google Scholar
  6. 6.
    Kekre, H.B., et al.: Face and gender recognition using principal component analysis. Int. J. Comput. Sci. Eng. (IJCSE) 02(04), 959–964 (2010)Google Scholar
  7. 7.
    Anton, H.: Elementary Linear Algebra 5e?. Wiley, Hoboken. ISBN 0-471-85223-6 (1987)Google Scholar
  8. 8.
    Utah State University—Spring 2012 STAT 5570: Statistical Bioinformatics, Notes 2.4Google Scholar
  9. 9.
    Yang, W.: Laplacian bidirectional PCA for face recognition. Neurocomputing (Elsevier) 74, 487–493 (2010)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Government Engineering College AjmerAjmerIndia
  2. 2.IIIT AllahabadAllahabadIndia

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