K-Means Graph Database Clustering and Matching for Fingerprint Recognition

Vaishali Pawar,Mukesh Zaveri

doi:10.4236/iim.2015.74019

Abstract

The graph can contain huge amount of data. It is heavily used for pattern recognition and matching tasks like symbol recognition, information retrieval, data mining etc. In all these applications, the objects or underlying data are represented in the form of graph and graph based matching is performed. The conventional algorithms of graph matching have higher complexity. This is because the most of the applications have large number of sub graphs and the matching of these sub graphs becomes computationally expensive. In this paper, we propose a graph based novel algorithm for fingerprint recognition. In our work we perform graph based clustering which reduces the computational complexity heavily. In our algorithm, we exploit structural features of the fingerprint for K-means clustering of the database. The proposed algorithm is evaluated using realtime fingerprint database and the simulation results show that our algorithm outperforms the existing algorithm for the same task.

Highlights

The graph is an important data structure for representing various multi-attributed data and their complex relations
[20] Method 2: Fingerprint recognition with K-NN clustering of the features and graph matching Method 3: Fingerprint recognition with K means graph database clustering The results are depicted
We notice that the graph clustering is reducing the graph matching time drastically than the time taken by graph matching without clustering

Summary

Introduction

The graph is an important data structure for representing various multi-attributed data and their complex relations. The graph plays an important role in the structural and syntactic methods of fingerprint classification In these methods the fingerprint patterns are represented by graphs or production rules in the grammar [6]. The graph based methods can effectively use relational attributes of the fingerprint to build the features graph. The graph based fingerprint matching algorithm is highly dependant on the combination of fingerprint features and corresponding graph model used to represent the fingerprint. Median graph is an important concept used in graph matching It acts as a representative for a set of graphs by extracting the essential information from them into a single prototype. The approximate K-Means based algorithm computes the generalized median graph [18] for graph clustering in content-based image retrieval and analysis of DNA structures [18]. Where U is the set of all graphs that can be constructed from a= given set S {g1, g2 , , gn} ⊂ U whose number of nodes is between 1 and the sum of all the nodes of the gi

The Proposed Algorithm

Tier 1 Clustering

The K-Nearest Neighbor Clustering

Tier 2 Clustering

Simulation Results

Conclusions