Face Recognition Based on Nonlinear Feature Approach | Science Publications

American Journal of Applied Sciences

G. Debashis¹  Eimad E.A. Abusham¹  Wong E. Kiong¹  Andrew T.B. Jin¹ 

关键词: Feature extraction;    LLE;    FDA;    LFDA;    manifold learning;   

DOI  :  10.3844/ajassp.2008.574.580

学科分类：自然科学（综合）

来源: Science Publications

【 摘 要 】

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.

【 授权许可】

Unknown