Shaikh Salleh, Sheikh Hussain and L. H., Chan and C. M., Ting (2010) Face biometrics based on principal component analysis and linear discriminant analysis. Journal of Computer Science, 6 (7). 693 - 699. ISSN 1549-3636
- Published Version
Problem statement: In facial biometrics, face features are used as the required human traits for automatic recognition. Feature extracted from face images are significant for face biometrics system performance. Approach: In this thesis, a framework of facial biometric was designed based on two subspace methods i.e., Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA). First, PCA is used for dimension reduction, where original face images are projected into lower-dimensional face representations. Second, LDA was proposed to provide a solution of better discriminant. Both PCA and LDA features were presented to Euclidean distance measurement which is conveniently used as a benchmark. The algorithms were evaluated in face identification and verification using a standard face database-AT and T and a locally collected database-CBE. Each database consists of 400 images and 320 images respectively. Results: LDA-based methods outperform PCA for both face identification and verification. For face identification, PCA achieves accuracy of 91.9% (AT and T) and 76.7% (CBE) while LDA 94.2% (AT and T) and 83.1% (CBE). For face verification, PCA achieves Equal Error Rate (EER) of 1.15% (AT and T), 7.3% (CBE) while LDA 0.78% (AT and T) and 5.81% (CBE). Conclusion/Recommendations: This study had proved that, when given sufficient training samples, LDA is able to provide better discriminant ability in feature extraction for face biometrics.
|Uncontrolled Keywords:||face biometrics, identification, verification, principal component analysis, linear discriminant analysis, equal error rate|
|Deposited By:||Liza Porijo|
|Deposited On:||27 Jun 2012 08:34|
|Last Modified:||14 Apr 2014 00:57|
Repository Staff Only: item control page