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Prof. Mona Fatma Mohamed Mursi Ahmed :: Publications:

Title:
“Appearance-based Face Recognition using PCA and LDA Approaches,” WSEAS TRANSACTIONS ON COMPUTERS, Transactions ID Number: 32-389, (submitted for publication), April 2009.
Authors: Hatim Aboalsamh, Ghazy Assassa, Mona Mursi, Hassan Mathkour,
Year: 2009
Keywords: Not Available
Journal: Not Available
Volume: Not Available
Issue: Not Available
Pages: Not Available
Publisher: Not Available
Local/International: International
Paper Link:
Full paper Not Available
Supplementary materials Not Available
Abstract:

Appearance-based face recognition techniques are appropriate for reducing the volume of computation for fast image analysis and classification. Face recognition plays a significant role in many security and forensic applications including person authentication for access control systems and person identification in real time video surveillance systems. This paper examines two appearance-based approaches for feature extraction and dimension reduction, namely, Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA). Numerical experiments were conducted on the ORL face database to investigate the effect of changing the number of training images, scaling factor, and the effect of feature vector length on the recognition rate. The results suggest that the effect of increasing the number of training images has more significance on the recognition rate than changing the image scale. Correlations obtained from numerical experiments on the ORL face datab! ase suggest that as the number of training images increases, PCA would yield slightly higher recognition rates. Keywords: Appearance-based, Face recognition, Principal components analysis (PCA), Eigenfaces, Linear discriminant analysis (LDA), Fisherfaces. EXTENSION of the file: .doc Special (Invited) Session: Organizer of the Session: This is an extended version of the paper presented at WSEAS conference Cambridge, Feb 2009 How Did you learn about congress: itlibrary@ksu.edu.sa IP ADDRESS: 86.51.207.141

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