Author:
Balti Ala,Hamdi Abdelaziz,Abid Sabeur,Ben Khelifa Mohamed Moncef,Sayadi Mounir
Abstract
This research introduces a novel MOMENTS-SVD vector for fingerprint identification, combining invariant moments and SVD (Singular Value Decomposition), enhanced by a modified PCA (Principal Component Analysis). Our method extracts unique fingerprint features using SVD and invariant moments, followed by classification with Euclidean distance and neural networks. The MOMENTS-SVD vector reduces computational complexity by outperforming current models. Using the Equal Error Rate (EER) and ROC curve, a comparative study across databases (CASIA V5, FVC 2002, 2004, 2006) assesses our method against ResNet, VGG19, Neuro Fuzzy, DCT Features, and Invariant Moments, proving enhanced accuracy and robustness.
Reference23 articles.
1. Fingerprint systems: sensors, image acquisition, interoperability and challenges;Abdul Cader;Sensors,2023
2. Fingerprint recognition using DCT features;Amornraksa;Electron. Lett.,2006
3. A new fingerprint identification approach based on SVD features;Balti,2014
4. Invariant and reduced features for Fingerprint Characterization;Balti;IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society, Montreal, QC, Canada.,2012
5. Fingerprint verification based on back propagation neural network;Balti;J. Control Eng. Appl. Informat.,2013