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A novel biometric feature extraction algorithm using two dimensional fisherface in 2dpca subspace for face recognition.
| Content Provider | CiteSeerX |
|---|---|
| Author | Mutelo, R. M. Woo, W. L. Dlay, S. S. |
| Abstract | Abstract:- This paper describes a novel algorithm, 2D-FPCA, for face feature extraction and representation. The new algorithm fuses the two dimensional Fisherface method with the two dimensional principal component analysis (2DPCA). Our algorithm operates on the two dimensional image matrices. Therefore a total image covariance matrix can be constructed directly using the original image matrices and its eigenvectors are derived for feature extraction. Similarly, the between and the within image covariance matrices are constructed and transformed to a 2DPCA subspace. The result is that 2D-FPCA is faster and yields greater recognition accuracy. The ORL database is used as a benchmark. The new algorithm achieves a recognition rate of 95.50 % compared to the recognition rate of 90.00 % for the Fisherface method. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Dimensional Fisherface Face Recognition Novel Biometric Feature Extraction Algorithm New Algorithm Recognition Rate Total Image Covariance Matrix Novel Algorithm Dimensional Fisherface Method 2dpca Subspace Dimensional Principal Component Analysis Orl Database Dimensional Image Matrix Original Image Matrix Fisherface Method Face Feature Extraction Feature Extraction Image Covariance Matrix Recognition Accuracy |
| Content Type | Text |
| Resource Type | Article |
