Feature Level Clustering of Large Biometric Database

Details Feature Level Clustering of Large Biometric Database Feature Level Clustering of Large Biometric Database

2010-02-02

arxiv.org/abs/1002.0383v1

Computer Science

Computer Vision and Pattern Recognition

4 pages, 2 figures, IAPR International Conference on Machine Vision Applications, 2009

Scientific paper

This paper proposes an efficient technique for partitioning large biometric database during identification. In this technique feature vector which comprises of global and local descriptors extracted from offline signature are used by fuzzy clustering technique to partition the database. As biometric features posses no natural order of sorting, thus it is difficult to index them alphabetically or numerically. Hence, some supervised criteria is required to partition the search space. At the time of identification the fuzziness criterion is introduced to find the nearest clusters for declaring the identity of query sample. The system is tested using bin-miss rate and performs better in comparison to traditional k-means approach.

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