Biometric Finger Vein Recognition Using Evolutionary Algorithm with Deep Learning

In recent years,the demand for biometric-based human recog-nition methods has drastically increased to meet the privacy and security requirements.Palm prints,palm veins,finger veins,fingerprints,hand veins and other anatomic and behavioral features are utilized in the development of different biometric recognition techniques.Amongst the available biometric recognition techniques,Finger Vein Recognition(FVR)is a general technique that analyzes the patterns of finger veins to authenticate the individuals.Deep Learning(DL)-based techniques have gained immense attention in the recent years,since it accomplishes excellent outcomes in various challenging domains such as computer vision,speech detection and Natural Language Processing(NLP).This technique is a natural fit to overcome the ever-increasing biomet-ric detection problems and cell phone authentication issues in airport security techniques.The current study presents an Automated Biometric Finger Vein Recognition using Evolutionary Algorithm with Deep Learning(ABFVR-EADL)model.The presented ABFVR-EADL model aims to accomplish bio-metric recognition using the patterns of the finger veins.Initially,the presented ABFVR-EADL model employs the histogram equalization technique to pre-process the input images.For feature extraction,the Salp Swarm Algorithm(SSA)with Densely-connected Networks(DenseNet-201)model is exploited,showing the proposed method’s novelty.Finally,the Deep-Stacked Denoising Autoencoder(DSAE)is utilized for biometric recognition.The proposed ABFVR-EADL method was experimentally validated using the benchmark databases,and the outcomes confirmed the productive performance of the proposed ABFVR-EADL model over other DL models.

Extraction of intersecting palm-vein and palmprint features for cancellable identity verification

A novel method based on the cross-modality intersecting features of the palm-vein and the palmprint is proposed for identity verification.Capitalising on the unique geometrical relationship between the two biometric modalities,the cross-modality intersecting points provides a stable set of features for identity verification.To facilitate flexibility in template changes,a template transformation is proposed.While maintaining non-invertibility,the template transformation allows transformation sizes beyond that offered by the con-ventional means.Extensive experiments using three public palm databases are conducted to verify the effectiveness the proposed system for identity recognition.

People Recognition by RGB and NIR Analysis from Digital Image Database Using Cross-Correlation and Wavelets

This document presents a framework for recognizing people by palm vein distribution analysis using cross-correlation based signatures to obtain descriptors. Haar wavelets are useful in reducing the number of features while maintaining high recognition rates. This experiment achieved 97.5% of individuals classified correctly with two levels of Haar wavelets. This study used twelve-version of RGB and NIR (near infrared) wavelength images per individual. One hundred people were studied;therefore 4,800 instances compose the complete database. A Multilayer Perceptron (MLP) was trained to improve the recognition rate in a k-fold cross-validation test with k = 10. Classification results using MLP neural network were obtained using Weka (open source machine learning software).

Finger vein recognition using weighted local binary pattern code based on a support vector machine

Finger vein recognition is a biometric technique which identifies individuals using their unique finger vein patterns. It is reported to have a high accuracy and rapid processing speed. In addition, it is impossible to steal a vein pattern located inside the finger. We propose a new identification method of finger vascular patterns using a weighted local binary pattern (LBP) and support vector machine (SVM). This research is novel in the following three ways. First, holistic codes are extracted through the LBP method without using a vein detection procedure. This reduces the processing time and the complexities in detecting finger vein patterns. Second, we classify the local areas from which the LBP codes are extracted into three categories based on the SVM classifier: local areas that include a large amount (LA), a medium amount (MA), and a small amount (SA) of vein patterns. Third, different weights are assigned to the extracted LBP code according to the local area type (LA, MA, and SA) from which the LBP codes were extracted. The optimal weights are determined empirically in terms of the accuracy of the finger vein recognition. Experimental results show that our equal error rate (EER) is significantly lower compared to that without the proposed method or using a conventional method.

Palm vein recognition method based on fusion of local Gabor histograms

Gabor features have been shown to be effective for palm vein recognition. This paper presents a novel feature representation method, implementing the fusion of local Gabor histograms （FLGH）, in order to improve the accuracy of palm vein recognition systems. A new local descriptor called local Gabor principal differences patterns （LGPDP） encodes the Gabor magnitude using the local maximum difference （LMD） operator. The corresponding Gabor phase patterns are encoded by local Gabor exclusive OR （XOR） patterns （LGXP）. Fisher＇s linear discriminant （FLD） method is then implemented to reduce the dimensionality of the feature representation. Low-dimensional Gabor magnitude and phase feature vectors are finally fused to enhance accuracy. Experimental results from Institute of Automation, Chinese Academy of sciences （CASIA） database show that the proposed FLGH method achieves better performance by utilizing score-level fusion. The equal error rate （EER） is 0.08%, which outperforms other conventional palm vein recognition methods （EER range from 2.87% to 0.16%）, e.g., the Laplacian palm, minutiae feature, Hessian phase, Eigenvein, local invariant features, mutual foreground local binary patterns （LBP）, and multi-sampling feature fusion methods.

Unconstrained Hand Dorsal Veins Image Database and Recognition System

Hand veins can be used effectively in biometric recognition since they are internal organs that,in contrast to fingerprints,are robust under external environment effects such as dirt and paper cuts.Moreover,they form a complex rich shape that is unique,even in identical twins,and allows a high degree of freedom.However,most currently employed hand-based biometric systems rely on hand-touch devices to capture images with the desired quality.Since the start of the COVID-19 pandemic,most handbased biometric systems have become undesirable due to their possible impact on the spread of the pandemic.Consequently,new contactless hand-based biometric recognition systems and databases are desired to keep up with the rising hygiene awareness.One contribution of this research is the creation of a database for hand dorsal veins images obtained contact-free with a variation in capturing distance and rotation angle.This database consists of 1548 images collected from 86 participants whose ages ranged from 19 to 84 years.For the other research contribution,a novel geometrical feature extraction method has been developed based on the Curvelet Transform.This method is useful for extracting robust rotation invariance features from vein images.The database attributes and the veins recognition results are analyzed to demonstrate their efficacy.