Research Article Open Access

Face Recognition Based on Nonlinear Feature Approach

Eimad E.A. Abusham, Andrew T.B. Jin, Wong E. Kiong and G. Debashis

Abstract

Feature extraction techniques are widely used to reduce the complexity high dimensional data. Nonlinear feature extraction via Locally Linear Embedding (LLE) has attracted much attention due to their high performance. In this paper, we proposed a novel approach for face recognition to address the challenging task of recognition using integration of nonlinear dimensional reduction Locally Linear Embedding integrated with Local Fisher Discriminant Analysis (LFDA) to improve the discriminating power of the extracted features by maximize between-class while within-class local structure is preserved. Extensive experimentation performed on the CMU-PIE database indicates that the proposed methodology outperforms Benchmark methods such as Principal Component Analysis (PCA), Fisher Discrimination Analysis (FDA). The results showed that 95% of recognition rate could be obtained using our proposed method.

American Journal of Applied Sciences
Volume 5 No. 5, 2008, 574-580

DOI: https://doi.org/10.3844/ajassp.2008.574.580

Submitted On: 1 September 2007 Published On: 31 May 2008

How to Cite: Abusham, E. E., Jin, A. T., Kiong, W. E. & Debashis, G. (2008). Face Recognition Based on Nonlinear Feature Approach. American Journal of Applied Sciences, 5(5), 574-580. https://doi.org/10.3844/ajassp.2008.574.580

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Keywords

  • Feature extraction
  • LLE
  • FDA
  • LFDA
  • manifold learning