Research on automatic fingerprint classification based on support vector machine

Automatic Finger classification is an important part of Fingerprint Automatic Identification System (FAIS). Its function is to provide a search system for large size database. Accurate classification can reduce searching time and expediate matching speed. Support Vector Machine (SVM) is a new learning technique based on Statistical Learning Theory (SLT). SVM was originally developed for two-class classification. It was extended to solve multi-class classification problem. A hierarchical SVM with clustering algorithm based on stepwise decomposition was established to intellectively classify 5 classes of fingerprints. The design principle was proposed and the classification algorithm was implemented. SVM not only has more solid theoretical foundation, it also has greater generalization ability as our experiment demonstrates. The experimental results show that: SVM is effective and surpasses other classical classification techniques.