An Efficient Automatic Overlapped Fingerprint Identification and Recognition Using ANFIS Classifier

Abstract

The Automatic Fingerprint Recognition System plays an important role in forensics and law enforcement applications. The objective of the proposed system in the current study is to identify and separate overlapped fingerprint images automatically using an Adaptive Neuro Fuzzy Inference System (ANFIS) Classifier. There are various issues that have been identified, which need to be addressed to develop the scope of light–out fingerprint recognition system. The latent fingerprint images can be overlapped in crime scenes. During investigations, there are several possibilities for acquiring damaged or overlapped fingerprint images. The proposed system analyzes and identifies the overlapped images using an ANFIS Classifier. This paper also proposes a novel algorithm for the separation of overlapped images. The proposed work is designed to retrieve fast and accurate data using fingerprint identification for the overlapped images. Extensive experiments are performed on the FVC 2006 DB1-A, DB2-A, NIST SD27 and SLF databases. The experimental results are highly promising and outperform the previous systems in identifying the overlapped images. Our proposed system separates those overlapped fingerprints more accurately and robustly. The achieved results confirmed that the proposed automatic fingerprint recognition system has higher possibility of overlapped fingerprint detection.