A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Statistics to Neural Networks: Theory and Pattern Recognition Applications
From Statistics to Neural Networks: Theory and Pattern Recognition Applications
On the Individuality of Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
High Confidence Visual Recognition of Persons by a Test of Statistical Independence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Writer Identification: Statistical Analysis and Dichotomizer
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Improved Techniques for an Iris Recognition System with High Performance
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Fast Image Template and Dictionary Matching Algorithms
ACCV '98 Proceedings of the Third Asian Conference on Computer Vision-Volume I - Volume I
Support Vector Machines: Training and Applications
Support Vector Machines: Training and Applications
Use of distance measures in handwriting analysis
Use of distance measures in handwriting analysis
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Guide to Biometrics
Personal Identification Based on Iris Texture Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
A review of information fusion techniques employed in iris recognition systems
International Journal of Advanced Intelligence Paradigms
| Hi-index | 0.00 |
Biometric authentication has been considered a model for quantitatively establishing the discriminative power of biometric data. The dichotomy model classifies two biometric samples as coming either from the same person or from two different people. This paper reviews features, distance measures, and classifiers used in iris authentication. For feature extraction we compare simple binary and multi-level 2D wavelet features. For distance measures we examine scalar distances such as Hamming and Euclidean, feature vector and histogram distances. Finally, for the classifiers we compare Bayes decision rule, nearest neighbor, artificial neural network, and support vector machines. Of the eleven different combinations tested, the best one uses multi-level 2D wavelet features, the histogram distance, and a support vector machine classifier.