Finger-based personal authentication: a comparison of feature-extraction methods based on principal component analysis, most discriminant features and regularised-direct linear discriminant analysis
N. Pavešić, Samo Ribarič, Burton Grad · IET Signal Processing · 2009
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.