Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometric Identification through Hand Geometry Measurements
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Biometrics: Identity Verification in a Networked World
Biometrics: Identity Verification in a Networked World
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Guide to Biometrics
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Face Recognition Using Landmark-Based Bidimensional Regression
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
How effective are landmarks and their geometry for face recognition?
Computer Vision and Image Understanding
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Biometrics: Personal Identification in Networked Society
Biometrics: Personal Identification in Networked Society
An efficient face verification method in a transformed domain
Pattern Recognition Letters
Performance of similarity measures based on histograms of local image feature vectors
Pattern Recognition Letters
Authentication of Individuals using Hand Geometry Biometrics: A Neural Network Approach
Neural Processing Letters
IEEE Transactions on Image Processing
Biometric dispersion matcher versus LDA
Pattern Recognition
On-line signature verification system with failure to enrol management
Pattern Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Score fusion in text-dependent speaker recognition systems
COST'10 Proceedings of the 2010 international conference on Analysis of Verbal and Nonverbal Communication and Enactment
A study on the consistency and significance of local features in off-line signature verification
Pattern Recognition Letters
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In this paper we propose a new classifier called a dispersion matcher. Our proposal is especially well adapted to those scenarios where a large number of classes and a small number of samples per class are available for training. This is the situation of biometric systems where just three to five measures per person are acquired during enrollment. This is just the opposite situation of other pattern recognition applications where a small number of classes and a large amount of training samples are available, such as handwritten digit recognition (10 classes) for ZIP code identification. The dispersion matcher trains a quadratic discriminant classifier to solve the dichotomy ''Do these two feature vectors belong to the same person?''. In this way, we solve an important set of topics: (a) we can classify an open world problem and we do not need to train the model again if a new user is added, (b) we find a natural solution for feature selection, (c) experimental results with a priori threshold provides good results. We evaluate the proposed system with hand-geometry and face recognition problems (identification and verification). In hand geometry, we get a minimum detection cost function (DCF) for verification of 0.21% and a maximum identification rate of 99.1%, which compares favorably with other state-of-the-art methods. In face verification we achieve 5.59% DCF and 92.77% identification rate, which also compares favorably with the literature.