No abstract

No abstract

No 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.

No abstract

No abstract

No abstract

Privacy preserving data mining is a novel research direction in data mining and statistical databases, which has recently been proposed in response to the concerns of preserving personal or sensible information derived from data mining algorithms. There have been two types of privacy proposed concerning data mining. The first type of privacy, called output privacy, is that the data is altered so that the mining result will preserve certain privacy. The second type of privacy, called input privacy, is that the data is manipulated so that the mining result is not affected or minimally affected. For output privacy in hiding association rules, current approaches require hidden rules or patterns to be given in advance. However, to specify hidden rules, entire data mining process needs to be executed. For some applications, only certain sensitive rules that contain sensitive items are required to hide. In this work, an algorithm SRH (Sensitive Rule Hiding) is proposed, to hide the sensitive rules that contain sensitive items, so that sensitive rules containing specified sensitive items on the right hand side of the rule cannot be inferred through association rule mining. Example illustrating the proposed approach is given. The characteristics of the algorithm are discussed.