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K-Means Graph Database Clustering and Matching for Fingerprint Recognition

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DOI: 10.4236/iim.2015.74019    5,301 Downloads   6,170 Views   Citations


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.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

Pawar, V. and Zaveri, M. (2015) K-Means Graph Database Clustering and Matching for Fingerprint Recognition. Intelligent Information Management, 7, 242-251. doi: 10.4236/iim.2015.74019.


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