DATA ACQUISITION AND RECOGNITION OF FINGERPRINTS WITH LVQ

Abstract

A typical biometric system has three distinct phases. These are biometric data acquisition, feature extraction, and decision-making. The first step, the acquisition phase, is extremely important. If high quality images are not obtained, the next phase cannot operate reliably. Fingerprint recognition remains as one of the most prominent biometric identification methods. In this paper, we develop a prototype optical-based fingerprints data acquisition system using a CCD digital still camera to capture a complete impression of finger area required for accurately identifying an individual and present an image-based approach for online fingerprint recognition with the objective to increase the overall matching performance. The fingerprint images are matched based on features extracted with an adaptive learning vector quantization (LVQ) neural network to yield peak recognition of 98.6% for a random set of 300 test prints (100 fingers × 3 images). This system can be adopted as a multi-modal biometrics where two or more fingers are matched.