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Finger-based personal authentication: a comparison of feature-extraction methods based on principal component analysis, most discriminant features and regularised-direct linear discriminant analysis

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3 Author(s)
Pavesic, N. ; Fac. of Electr. Eng., Univ. of Ljubljana, Ljubljana ; Ribaric, S. ; Grad, B.

In this study, feature-extraction methods based on principal component analysis, most discriminant features, and regularised-direct linear discriminant analysis (RD-LDA) are tested and compared in an experimental finger-based personal authentication system. The system is multimodal and based on features extracted from eight regions of the hand: four fingerprints (the prints of the finger tips) and four digitprints (the prints of the fingers between the first and third phalanges). All of the regions are extracted from one-shot grey-level images of the palmar surface of four fingers of the right hand. The identification and verification experiments were conducted on a database consisting of 1840 finger images (184 people). The experiments showed that the best results were obtained with the RD-LDA-based feature-extraction method -99.98% correct identification for 920 tests and an equal error rate of 0.01% for 64170 verification tests.

Published in:

Signal Processing, IET  (Volume:3 ,  Issue: 4 )