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

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.