A New Finger-Vein Recognition System Using the Complete Local Binary Pattern and the Phase Only Correlation

Abstract

A new system for finger-vein recognition is proposed based on the Complete Local Binary pattern (CLBP) as a feature extractor and the Phase Only Correlation (POC) for post-processing alignment and for speeding up the system. The CLBP produces three components of image descriptors and thus holds more details compared to the previous methods such as the Local Binary Pattern (LBP), the Local Directional Pattern (LDP), the Local Line Binary Pattern (LLBP), the Repeated Line Tracking (RLT), the Maximum Curvature (MC) and the Wide Line Detector (WLD). In the proposed system, POC is used for two purposes. First, to increase the performance of the system the alignment between the CLBP components of the test image and the enrolled CLBP components are performed. Second, to speed up the matching stage, a portion of the enrolled images is excluded that are highly misaligned with the test image from the Hamming Distance (HD) measure competition in the matching stage. To make the system more secure against attacks targeting personal information, only CLBP components are enrolled in the system and the alignment process POC is implemented on these components without the need to original images. For image pre-processing a novel scheme of preprocessing methods is adopted including finger-vein localization, alignment, and the Region-Of-Interest (ROI) extraction and enhancement. Two databases, UTFVP and SDUMLA-HMT, are used to evaluate the performance of the system. The results have shown that the values for the Identification Recognition Rate (IRR) and the Equal Error Rate (EER) are respectively (99.66%) and (0.139) for the UTFVP database and (98.95%, and 0.53%) for SDUMLA-HMT database. These results are competitive compared to those achieved by the state-of-art systems.

FFV-MBC: A Novel Fused Finger-Vein Recognition Method Based on Monogenic Binary Coding

“…Some methods focus on local features for finger-vein recognition, i.e., local binary pattern [21], local line binary pattern (LLBP) [22], etc. Recently, Mustafa et al [23] proposed a new finger-vein recognition system using a combination of complete local binary pattern and phase-only correlation. Compared with the classical local feature extraction, this method can extract more detailed features.…”

Improving Iris Recognition Accuracy Using Gabor Kernels With Near-Horizontal Orientations

“…Minhas and Javed, [19] used multichannel Gabor filters globally and locally by dividing the image into four sub-images. They used a number of banks for Gabor filters (15,20,25,30,35) using six different wavelength (3,5,7,9,11) and five different orientations (0°, 30°, 60°, 90°, 120°). They achieved the best accuracy of 98.99% when Gabor filter is applied locally with 35 filters using CASIA-1 dataset.…”

“…Many biometric recognition systems have already been developed using various types of human traits such as iris, finger vein, face, fingerprints, palmprint, gait, etc., [7][8][9][10][11][12][13][14][15]. However, human iris has brought great attention because it has a unique texture for each person.…”

Finger Veins Verification by Exploiting the Deep Learning Technique

“…The enrolled database does not contain the original images to avoid attacks targeting personal information. The used databases contained six images per person for only three fingers of each hand (index, middle, and ring) [22]. In 2021, Shaaban and Mahdi presented an Enhanced extraction method of ROI using machine learning.…”