Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Visual Learning for Object Representation
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
Face Recognition by Elastic Bunch Graph Matching
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
Principal Manifolds and Bayesian Subspaces for Visual Recognition
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Journal of Cognitive Neuroscience
Robust coding schemes for indexing and retrieval from large face databases
IEEE Transactions on Image Processing
Face recognition with disguise and single gallery images
Image and Vision Computing
Detection of Local Mura Defects in TFT-LCD Using Machine Vision
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
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We introduce in this paper a novel Independent Gabor wavelet Features (IGF) method for face recognition. The IGF method derives first an augmented Gabor feature vector based upon the Gabor wavelet transformation of face images and using different orientation and scale local features. Independent Component Analysis (ICA) operates then on the Gabor feature vector subject to sensitivity analysis for the ICA transformation. Finally, the IGF method applies the Probabilistic Reasoning Model for classification by exploiting the independence properties between the feature components derived by the ICA. The feasibility of the new IGF method has been successfully tested on face recognition using 600 FERET frontal face images corresponding to 200 subjects whose facial expressions and lighting conditions may vary.