Fingerprint image classification using local diagonal and directional extrema patterns

Abstract

Proper classification of fingerprints still poses difficult issues in large-scale databases due to ambiguity in intraclass and interclass structures, discontinuity in low-quality images, and ridges. To address these challenges, we propose a feature named local diagonal and directional extrema pattern (LDDEP) as a descriptor for classification of fingerprints. The proposed method utilizes first-order derivatives to find values and indices of local diagonal and directional extremas. The local extrema values are then compared with the central pixel intensity value to find the correlation with the neighbors. Eventually, the descriptor is generated with the help of the indices and local extrema values. Furthermore, the proposed descriptor is fed into K-nearest neighbor and support vector machine (SVM) for classifying the fingerprint images into four and five groups, respectively. The LDDEP descriptor is compared with the existing methods on two databases, namely National Institute of Standards Technology Special Database 4 (NIST SD 4) and Fingerprint Verification Competition (FVC). Our experiments have shown that, on the 4000 image NIST SD 4 test dataset, the proposed descriptor achieved a classification accuracy of 95.15% for five classes and 96.85% for four classes for half of the dataset, and an accuracy of 95.5% for five classes and 96.63% for four classes for the entire test dataset using SVM classifier. Similarly, FVC databases for the LDDEP descriptor gave classification accuracy of 98.2% using SVM classifier. The proposed method gave higher accuracies compared to the existing methods.