Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition through Geometrical Features
ECCV '92 Proceedings of the Second European Conference on Computer Vision
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
Improving kernel Fisher discriminant analysis for face recognition
IEEE Transactions on Circuits and Systems for Video Technology
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Hi-index | 0.00 |
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.