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Title |
Comparing The Performance of Principal Component Analysis and RBF Network for Face Recognition Using Locally Linear Embedding |
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Author |
Eimad E.A.A.Abusham, David Ngo Andrew Teoh |
| Citation |
Vol. 6 No. 4 pp. 25-29 |
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Abstract |
Among the many methods proposed in the literature for face recognition, those relying on face manifold have been explored with great interest in the last few years. In those methods the face images are initially subjected to dimensional reduction and then applied to a classifier. In this paper we have proposed and developed two novel approaches for face recognition to address the challenging task of recognition using a fusion of nonlinear dimensional reduction; Locally Linear Embedding (LLE) integrated with Principal Component Analysis (LLEPCA) and LLE with RBF networks (LLERBF) and then we evaluate and compare their performance. Extensive experiments using the CMU AMP Face EXpression Database and JAFFE databases indicate that the more general model underlying the RBF classifier does not bring any significant improved performance as compared with the Principal Component Analysis approach. |
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Keywords |
Biometrics,Locally Linear Embedding, nonlinear manifold,PCA,RBF. |
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