Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
The nature of statistical learning theory
The nature of statistical learning theory
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition System Using Local Autocorrelations and Multiscale Integration
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
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Looking at People: Sensing for Ubiquitous and Wearable Computing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
Robust recognition using eigenimages
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
A Support Vector Machine with a Hybrid Kernel and Minimal Vapnik-Chervonenkis Dimension
IEEE Transactions on Knowledge and Data Engineering
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition by Stepwise Nonparametric Margin Maximum Criterion
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Feature selection for linear support vector machines
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Extension of higher order local autocorrelation features
Pattern Recognition
Interest Operator versus Gabor filtering for facial imagery classification
Pattern Recognition Letters
Journal of Cognitive Neuroscience
BDPCA plus LDA: a novel fast feature extraction technique for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Image Processing
Robust coding schemes for indexing and retrieval from large face databases
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Facial expression recognition in JAFFE dataset based on Gaussian process classification
IEEE Transactions on Neural Networks
Pattern Recognition Letters
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This paper introduces a novel Fisher discriminant classifier based on the interest filter representation for face recognition. Our interest Fisher classifier (IFC), which is robust to illumination and facial expression variability, applies the Fisher linear discriminant (FLD) to an augmented interest feature vector derived from interest filter representation of face images. The novelty of this paper comes from our proposed interest filter: the interest operator can reveal the local activity of the images but suffer from some drawbacks and we improve the capability of the interest operator and propose a multi-orientation and multi-scale interest filter. In particular, we carry out comparative studies of different similarity measures applied to various classifiers. We also perform comparative experimental studies of various face recognition schemes, including our novel IFC method, the Eigenfaces and the Fisherfaces methods, the combination of interest operator and the Eigenfaces method, the combination of interest operator and the Fisherfaces method, the Eigenfaces on the augmented interest feature vectors and other popular subspace methods. The feasibility of the new IFC method has been successfully tested on two data sets from the FERET and AR databases. The novel IFC method achieves the highest accuracy on face recognition on both two datasets.