Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
A Multichannel Approach to Fingerprint Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint Classification by Directional Image Partitioning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Target detection in SAR imagery by genetic programming
Advances in Engineering Software
A Combination Fingerprint Classifier
IEEE Transactions on Pattern Analysis and Machine Intelligence - Graph Algorithms and Computer Vision
Systematic Methods for the Computation of the Directional Fields and Singular Points of Fingerprints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evolution of Vehicle Detectors for Infrared Line Scan Imagery
EvoIASP '99/EuroEcTel '99 Proceedings of the First European Workshops on Evolutionary Image Analysis, Signal Processing and Telecommunications
Genetic Programming for Automatic Target Classification and Recognition
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Genetic Programming for Feature Detection and Image Segmentation
Selected Papers from AISB Workshop on Evolutionary Computing
Evolutionary feature synthesis for facial expression recognition
Pattern Recognition Letters - Special issue: Evolutionary computer vision and image understanding
Enhanced SEA algorithm and fingerprint classification
International Journal of Computer Applications in Technology
Hi-index | 0.00 |
In this paper, we present a fingerprint classification approach based on a novel feature-learning algorithm. Unlike current research for fingerprint classification that generally uses visually meaningful features, our approach is based on Genetic Programming (GP), which learns to discover composite operators and features that are evolved from combinations of primitive image processing operations. Our experimental results show that our approach can find good composite operators to effectively extract useful features. Using a Bayesian classifier, without rejecting any fingerprints from NIST-4, the correct rates for 4 and 5-class classification are 93.2% and 91.2% respectively, which compare favorably and have advantages over the best results published to date.