TITLE:
K-Means Graph Database Clustering and Matching for Fingerprint Recognition
AUTHORS:
Vaishali Pawar, Mukesh Zaveri
KEYWORDS:
Pattern Recognition, Fingerprint Matching, Graph Matching, Clustering
JOURNAL NAME:
Intelligent Information Management,
Vol.7 No.4,
July
30,
2015
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.