Title :
Fuzzy geometrical features for identifying distorted overlapping fingerprints
Author :
Pal, Sankar K. ; Sarbadhikari, Suptendra Nath
Author_Institution :
Machine Intelligence Unit, Indian Stat. Inst., Calcutta, India
Abstract :
Overlapping fingerprints are naturally abundant and pose great difficulty for proper identification. We demonstrate the effectiveness of fuzzy geometrical features for classifying distorted overlapping fingerprints directly from raw unprocessed images. The fuzzy geometrical features viz., length, height and index of area coverage are found to be the best for classifying these patterns when Bayes´, k-NN (with k=1, 3, 5) and MLP (multilayer perceptron) classifiers are used. The overall performance is best for MLP, followed by 1-NN
Keywords :
Bayes methods; feature extraction; fingerprint identification; fuzzy systems; image classification; multilayer perceptrons; Bayes´ classifiers; classifying distorted overlapping fingerprints; distorted overlapping fingerprints; fuzzy geometrical features; height; identification; index of area coverage; k-NN classifiers; length; multilayer perceptron; nearest neighbor classifier; pattern recognition; performance; raw unprocessed images; Concurrent computing; Fingerprint recognition; Fuzzy sets; Image matching; Law enforcement; Machine intelligence; Neural networks; Pattern matching; Pattern recognition; Uncertainty;
Conference_Titel :
Information, Communications and Signal Processing, 1997. ICICS., Proceedings of 1997 International Conference on
Print_ISBN :
0-7803-3676-3
DOI :
10.1109/ICICS.1997.652249
