A Robust Automatic Fingerprint Recognition System Using Multi-Connection Hopfield Neural Network

Abstract

Automatic fingerprint recognition has received significant attention because of its excellent fingerprint stability characteristics. Fingerprints will now explode in popularity as online stores allow payments and secure smartphones. The main objective of this study is to develop a fingerprint recognition system to identify individuals by using a new method, termed as ‘Multi-Connection Hopfield Neural Network’. This might pave the way for the development of a more robust fingerprint recognition system that is more accurate and capable of working in noisy environments. Furthermore, such a system makes operations less complex and saves memory by employing additional tranches of auto-associative memory. This study uses three databases, namely FVC-2004, International NIST-4, and internal database, containing 80, 2000, and 2000 fingerprint images. The proposed fingerprint recognition system achieves 99.65% accuracy without noise. This study also shows that the proposed system works well in the presence of noise and achieves 96.07% accuracy in the presence of 50% random noise.

Finger Vein Recognition Based on Multi-Features Fusion

“…In today's world, where security is becoming more important, personal authentication methods are also updating themselves to close existing security gaps with the developing technology. Although some Biometric Recognition methods, such as fingerprint [1][2][3], palmprint [4,5], retina [6][7][8], iris [8,9], and face recognition [10,11], used for this purpose, are used for tracking, entry-exit access permission control, and security purposes, they have some disadvantages compared to FV recognition. Among them, fingerprint and palmprint recognition systems create a security vulnerability because of unable to control whether the tissue is alive or not, being on the exterior surface of the body, and copied easily.…”