Convolutional Neural Network with Convolutional Block Attention Mechanism for Fingerprint Forgery Detection
Muntather Almusawi, Srinivas Aluvala, S Trisheela, Mukesh Soni, R.B. Revathi · 2024
The detection of fingerprint liveness has affected through spoofing, that is the major threat for fingerprint-based biometric systems. The issue of forgery detection is well studied and forged fingerprints gives huge impact of outcomes in biometric depended on security systems. In this research, the Convolutional Neural Network (CNN) with Convolutional Block Attention Mechanism (CBAM) for the detection of forgery in fingerprint images. The dataset used for this research are LivDet-2013 and LivDet-2015 and it is pre-processed by using Circular Hough Transform (CHT) method. Then, the features are extracted by using the Local Binary Pattern (LBP) method that extracts the meaningful features. The detection and classification are performed by using CNN with CBAM method that focuses much on detected patterns and detected the forgery with high accuracy. The proposed CNN with CBAM method attained 98.12% accuracy on LivDet-2013 and 97.05% accuracy on LivDet-2015 datasets while compared to existing methods like Hybrid Fingerprint Presentation Attack Detection (HyFiPAD).