Classification of fingerprint images using a neural network

Fingerprints are an effective means of identifying individuals, and several fingerprint comparison systems have already been developed. However, since it becomes difficult to recognize fingerprints with ease as the number and diversity of fingerprint patterns increases, it becomes desirable to create an efficient database for fingerprint matching with the goal of developing an automatic fingerprint classification system. Here an automatic classification system is imposed for fingerprint images in which the discrimination system is a multilayer neural network composed of five subnetworks to carry out macrofeature extraction on the fingerprint image. A total of 50 training examples, 10 per category, and 500 testing samples were presented to the network. According to the obtained results, the network achieved an 86 percent classification rate for the first candidate and a 99 percent classification rate for the first two candidates. The state of the network was analyzed after training, and the reasons for the system effectiveness and its classification error were studied.