Face recognition based on nonlinear feature approach

Eimad Eldin Abdu Abusham, Andrew T.B. Jin, Wong E. Kiong, G. Debashis

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)574-580
Number of pages7
JournalAmerican Journal of Applied Sciences
Issue number5
Publication statusPublished - 2008

All Science Journal Classification (ASJC) codes

  • General


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