The cortex transform: rapid computation of simulated neural images
Computer Vision, Graphics, and Image Processing
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: The Problem of Compensating for Changes in Illumination Direction
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Classifying Facial Attributes Using a 2-D Gabor Wavelet and Discriminant Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gabor Wavelets Transform and Extended Nearest Feature Space Classifier for Face Recognition
ICIG '04 Proceedings of the Third International Conference on Image and Graphics
The Representation and Matching of Pictorial Structures
IEEE Transactions on Computers
Journal of Cognitive Neuroscience
Multiresolution face recognition
Image and Vision Computing
IEEE Transactions on Image Processing
Hi-index | 0.00 |
This paper proposes and analyses the pyramidal Gabor eigenface (PGE) algorithm for face recognition. It can be realised directly in the spatial domain by using one-dimensional (1D) filter masks to extract the Gabor facial features. This is in contrast to the two-dimensional (2D) Fourier implementation that must be applied in both frequency and spatial domains in the general 2D Gabor-based facial feature extraction methods. Eigenface decomposition is then used to further reduce the redundancy of these face features. Because, Gabor features are characterised by strong spatial locality with scale and orientation selectivity, they can cope with variation problems due to illumination, facial expression change and orientation. Eigenface cannot handle such variations. The analysis of the algorithm and the experimental results using AT&T (formally Olivetti) face database show that the cost of the algorithm and the number of Gabor features in the PGE algorithm are significantly lower than the ones in the classic 2D Gabor wavelet-based methods and have better recognition results.