engleski

Finger-Based Personal Authentication: a Comparison of Feature-Extraction Methods Based on Principal Component Analysis, Most Discriminant Features and Regularised-Direct Linear Discriminant Analysis

In this paper, feature-extraction methods based on Principal Component Analysis (PCA), Most Discriminant Features (MDF), and Regularized-Direct Linear Discriminant Analysis (RD-LDA) are tested and compared in an experimental fingerbased 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 RDLDA- based feature-extraction method − 99.98% correct identification for 920 tests and an Equal Error Rate (EER) of 0.01% for 64170 verification tests.

Biometrics; LDA; MDF; RD-LDA; Feature Extraction; Authentication; Finger

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