Neural network based automatic fingerprint recognition system for overlapped latent images

Abstract

Automatic Fingerprint Recognition System (AFRS) is getting advanced as major distinct in the field of Biometrics. There are a number of difficult issues that need to be addressed in order to develop the scope for AFRS. In this regard designing challenges are non linear distortion, low quality image , segmentation, sensor noise, skin conditions, overlapping, inter class similarity, intra class variations and template aging. In crime scenes, the latent images can be merged with some background images or more number of fingerprint images from same person or different person can be overlapped. During investigation several possibilities are them to acquire damaged or un separated fingerprint image. The suspected criminals can’t be identified and recognized using such kind of images. In forensics, the matching accuracy of latent is extremely critical even if it involves some degree of manual intervention by latent examiners including manual markup. An overlapped fingerprint image must be able to split for fingerprint identification and recognition. This paper developed an algorithm to separate overlapping latent images. The proposed AFRS analyzes and design a fingerprint recognition system for overlapped latent images. The planned work is to formulate with accurate and fast data retrieval using one-to-N fingerprint identification for overlapped images. Extensive experiments are performed on the SLF databases, NIST SD27, FVC DB1, DB2 databases and evaluate rank-1 identification rate. The results show that the proposed system can separate overlapped fingerprint more accurately and robustly and it consequently improve the fingerprint recognition accuracy of AFRS.