Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Representation Using 2D Gabor Wavelets
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Object Classes and Image Synthesis From a Single Example Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for the Learning of Weights in Discrimination Functions Using a Priori Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Feature Selection for Pose Invariant Face Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Face Recognition Based on Fitting a 3D Morphable Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Based on Frontal Views Generated from Non-Frontal Images
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Journal of Cognitive Neuroscience
Face recognition using ada-boosted gabor features
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
AdaBoost gabor fisher classifier for face recognition
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
IEEE Transactions on Image Processing
Independent component analysis of Gabor features for face recognition
IEEE Transactions on Neural Networks
Analysis of variance of Gabor filter banks parameters for optimal face recognition
Pattern Recognition Letters
Discriminant phase component for face recognition
Journal of Electrical and Computer Engineering
Face recognition using Weber local descriptors
Neurocomputing
Hi-index | 0.00 |
Gabor wavelets are widely employed in face representation to decompose face images into their spatial-frequency domains. The Gabor wavelet transform, however, introduces very high dimensional data. To reduce this dimensionality, uniform sampling of Gabor features has traditionally been used. Since uniform sampling equally treats all the features, it can lead to a loss of important features while retaining trivial ones. In this paper, we propose a new face representation method that employs nonuniform multilevel selection of Gabor features. The proposed method is based on the local statistics of the Gabor features and is implemented using a coarse-to-fine hierarchical strategy. Gabor features that correspond to important face regions are automatically selected and sampled finer than other features. The nonuniformly extracted Gabor features are then classified using principal component analysis and/or linear discriminant analysis for the purpose of face recognition. To verify the effectiveness of the proposed method, experiments have been conducted on benchmark face image databases where the images vary in illumination, expression, pose, and scale. Compared with the methods that use the original gray-scale image with 4096-dimensional data and uniform sampling with 2560-dimensional data, the proposed method results in a significantly higher recognition rate, with a substantial lower dimension of around 700. The experimental results also show that the proposed method works well not only when multiple sample images are available for training but also when only one sample image is available for each person. The proposed face representation method has the advantages of low complexity, low dimensionality, and high discriminance.