EXTRACTING DIRECTIONAL INFORMATION FOR THE RECOGNITION OF FINGERPRINTS BY pRAM NETWORKS

A fingerprint processing and identification system based on pRAM neural networks is described. Firstly, a condensed numerical representation of a fingerprint is obtained which comprises a set of local directional images in two dimensions. Secondly, the core and delta points are identified, which are used as points of registration for the matching process. Finally, the input fingerprint can be matched with a set of reference fingerprints and the system displays the 10 best matching samples. The fingerprint is converted into a matrix of 17 × 17 directional images which are quantised to 8 levels. A two layer 6-pRAM pyramidal neural network was designed and trained by a reinforcement self-organising algorithm with an adaptive learning rate and Gaussian noise injection. The recognition rate is 86.4% for the best match. However, the system displays the 10 best matching samples so that these samples can then be manually inspected which is optimum for practical applications.