Modified Maximum Curvature Method (MMCM) and Logistic Regression: A Hybrid Architecture for Finger Vein Biometric Recognition System

Abstract

The finger vein authentication system is a prominent field in biometric-based research that prevents identity theft by forgery or spoofing. However, as the finger images are affected by many environmental factors such as illumination or shifting during imaging, they are often noisy and have irregularity in thickness or brightness which can cause a decline in the verification accuracy. Therefore, a meticulous finger vein pattern extraction method along with an accurate classification is necessary. Though the Maximum Curvature Method (MCM) gives promising verification accuracy, it fails to tackle the stated limitations. For this purpose, we proposed a Modified Maximum Curvature Method (MMCM) for vein extraction. In this paper, a hybrid architecture for finger vein biometric recognition system is stated with the combination of proposed MMCM and Logistic Regression (LR) machine learning classifier. Proposed MMCM incorporates Finger Region Extraction, Image Enhancement using Contrast Limited Adaptive Histogram Equalization (CLAHE), and Affine transform Normalization. The authentication is then carried out by fusing the proposed feature extraction with a set of Machine Learning Classifiers and evaluated based on their Equal Error Rate (EER) on the public database SDUMLA-HMT. The combination of MMCM vein extraction and LR classifier gives a satisfactory low EER of 0.043.