Fingerprint Classification Based on Novel Directional Features Using Neural Network
نویسندگان
چکیده
The retrieval effectiveness of Automatic Fingerprint Recognition Systems can be improved greatly by restricting the search of a fingerprint to a limited scope. Fingerprint classification refers to the process of assigning a fingerprint to a predefined class predominantly based on global features. In this paper, an efficient compact fixed size feature vector based on directional variance for representing fingerprints is proposed and these features have been used to classify fingerprint images into five established categories of the Galton-Henry classification scheme using a feed forward Artificial Neural Network (ANN). Backpropagation algorithm is used to train the ANN on a set of fingerprint images. The performance of the proposed classification system is measured in terms of the accuracy i.e. number of correctly classified fingerprints, tested on standard FVC2002 fingerprint database, Db1 and Db2 datasets that contain images of various qualities and types. Experimental results demonstrate that the suggested fixed size low dimensional fingerprint representation using the designated neural network classifier yields high accuracy classification rate.
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