Fingerprint recognition system
Date
2022-05
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Abstract
Fingerprint recognition is one of the most popular biometric techniques in personal
identification. The widespread use of fingerprint recognition as a biometric is because each
fingerprints pattern of ridges and valleys is unique and does not vary with time and age.
While there are several algorithms or methods for fingerprint recognition systems, the quest
to develop a robust fingerprint recognition system remains a significant research area. One
major challenge in designing a system for fingerprint recognition is its ability to perform
well on both full and partial fingerprint images. Most fingerprint recognition systems
developed so far use minutiae-based algorithms which tend to perform well under full
fingerprint but poorly under partial occlusion. In partial occlusion, the minutiae, which are
the core points of the fingerprints, get completely distorted. The distortion of minutiae
makes minutiae-based algorithms perform poorly. Therefore, this study proposed a novel
non-minutiae-based algorithm that adapts the Fisherface and Eigenface method from facial
recognition. The proposed algorithm is insensitive to partial occlusion. The Eigenface and
Fisherface methods were tested on FVC 2002 Datasets and yielded an accuracy of 86.67%
and 90% respectively. These accuracy results indicates that there is a possibility of
recognizing fingerprint images using non-minutiae-based algorithms from different domain.
Description
Undergraduate thesis submitted to the Department of Computer Science and Information Systems, Ashesi University, in partial fulfillment of Bachelor of Science degree in Management Information Systems, May 2022
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Undergraduate Thesis
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Keywords
biometrics