Palm and hand vein-based fuzzy vault generation scheme for multibiometric cryptosystem

Template security of biometric systems is a vital issue and needs critical focus. The importance lies in the fact that unlike passwords, stolen biometric templates cannot be revoked. Hence, the biometric templates cannot be stored in plain format and needs strong protection against any forgery. In this paper, we present a technique to generate face and palm vein-based fuzzy vault for multi-biometric cryptosystem. Here, initially the input images are pre-processed using various processes to make images fit for further processing. In our proposed method, the features are extracted from the processed face and palm vein images by finding out unique common points. The chaff points are added to the already extracted points to obtain the combined feature vector. The secret key points which are generated based on the user key input (by using proposed method) are added to the combined feature vector to have the fuzzy vault. For decoding, the multi-modal biometric template from palm vein and face image is constructed and is combined with the stored fuzzy vault to generate the final key. Finally, the experimentation is conducted using the palm vein and face database available in the CASIA and JAFFE database. The evaluation metrics employed are FMR (False Match Ratio) and GMR (Genuine Match Ratio). From the metric values obtained for the proposed system, we can infer that the system has performed well.

Biometrics recognition takes advantage of feature extraction and pattern recognition to analyze the physical and behavioral characteristics of biological individuals to achieve the purpose of individual identification. As a typical biometric technology, palm print and palm vein have the characteristics of high recognition rate, stable features, easy location and good image quality, which have attracted the attention of researchers. This paper designs and develops a multispectral palm print and palm vein acquisition platform, which can quickly acquire palm spectrum and palm vein multispectral images with seven different wavelengths. We propose a multispectral palm print palmar vein recognition framework, and feature-level image fusion is performed after extracting features of palm print palmar vein images at different wavelengths. Through the multispectral palm print palm vein image fusion experiment, a more feasible multispectral palm print and palm vein image fusion scheme is proposed. Based on the results of image fusion, we further propose an improved convolutional neural network (CNN) for model training to achieve identity recognition based on multispectral palm print palm vein images. Finally, the effects of different CNN network structures and learning rates on the recognition results were analyzed and compared experimentally.

Biometric computing offers an effective approach to personal identification based on unique, stable physical or behavioral characteristics. A reliable and robust personal verification approach using palm vein patterns is presented in this paper. This approach has lower computational and memory requirements and a higher recognition accuracy than similar methods. In my work, a near-infrared charge-coupled device camera is adopted as an input device for capturing palm vein images, since it can provide low-cost, non-contact imaging. In the proposed approach, two finger-webs are automatically selected to define the region of interest (ROI) in the palm vein images. Modified two-directional two-dimensional linear discriminant analysis (\(\text{(2D)}^{2}\text{ LDA}\)), which performs an alternate two-dimensional LDA (2DLDA) in the column direction of images in the 2DLDA subspace, is proposed to exploit the correlation between rows and columns of palm vein features inside the ROI. The major advantage of the method is that it requires fewer coefficients for efficient palm vein image representation and recognition. A total of 4,140 palm vein images were collected from 207 persons to verify the validity of the proposed palm vein recognition approach. High accuracies (\(>\)99 %) have been obtained by the proposed method, and the speed of the method (response time \(<\)0.75 s) is rapid enough for real-time recognition. Experimental results demonstrate that the proposed approach is feasible and effective for palm vein recognition.

In this paper, we propose a palm vein recognition system that combines two approaches using a decision-level fusion strategy. The first approach employs Binarized Statistical Image Features (BSIF) descriptor method on five overlapping sub-regions of palm vein images and the second approach uses a convolutional neural networks (CNN) model on each palm vein image. In the first approach, texture-based features of five overlapping sub-regions on the palm vein image are extracted using the powerful BSIF method and the scores obtained by the matching step of the system are fused with score-level fusion strategy. In the second approach, a CNN model is used to train the system using the whole image. Afterwards, the decisions of two approaches are gathered separately and a final decision is obtained by fusing the two decisions. Experimental results on CASIA, FYO, PUT, VERA and Tongji Contactless Palm Vein databases showed that the proposed method compared favorably against other similar systems.

When collecting palm vein images, it is easy to be affected by external factors such as light source and placement angle, which result in poor recognition accuracy. In this paper, a new method, which involve a new method of region of interest segmentation and an improved palm recognition method of VGG16 deep convolutional neural network, was proposed to promote the recognition accuracy and be well adapted to the actual application scenarios. Firstly, the original palm vein image is obtained through profile original image, positioning key point of original image, and extract region of interest image. Afterwards, the adaptive histogram equalization technique and Gaussian Filters are utilized to improve image quality. Secondly, for palm vein image recognition application scenarios, the output of the convolutional layer of the VGG-16 convolutional neural network is standardized in batches, and the attention mechanism is introduced to optimize the VGG-16 neural network. The optimized network is used for feature extraction and recognition of palm vein images. Thirdly, data enhancement was performed on the public Polyu multispectral palm vein data set, and then a large number of experiments were carried out, and the best recognition rate was 99.57%.

In this paper, recognition of the hand vein patterns approach is proposed employing the Convolutional Neural Network (CNN). This approach is routinely well-learned in what way to get features from the main pattern using Region of Interest (ROI). Though, the poor quality of the hand vein image still attitudes an unlimited strain to the extension leads of its usability. Firstly, by applying the method of Generative adversarial networks (GAN) data augmentation the performance gain of adding GAN generated data exceeds that of adding more true images, and apply ROI in a hand vein image feature extraction is studied initially. Secondly, the suggested approach is tested on the data sets of hand veins to decrease the overfitting in the fully connecting layer of CNN which this model proves the most effective one. In total, 1575 hand vein images from 100 subjects are applied to authorize the proposed approach for hand vein. A high accuracy (>99.8%) and low False Rejection Rate(FRR) (<0.99%) were achieved by applying the suggested approach, when compared with the existing CNN classifiers, indicating the efficiency of the suggested approach.

In order to improve the security and reliability of the identification system, a combination recognition method of palmprint and palm vein based on gray surface matching is proposed. Firstly, the palmprint and palm vein image are seen as a kind of gray surface features of the three-dimensional space. Secondly, palmprint and palm vein image are combined. Furthermore the difference surface is get from the subtraction operation between the different combinations of gray surfaces, finally the standard deviation can be calculated from it, which will be a basis for the classification of different images. Contrast experiments are carried out with typical and popular methods in the standard contact public database of the Hong Kong Polytechnic University (PolyU). The results show that the method can obtain a minimum of 0.3011% equal error rate (EER), and the recognition time is only 0.0017s. These show the superiority and validity of the method which has a good application prospect.

Biometric recognition is noteworthy method for recognition of person in recent years. Here, a common concern is biometric security which is the privacy issues derived from storage and misuses of the template data. In order to handle this issue, researches have proposed different algorithms to be confronted by security of biometric systems. Two major ways are, (1) Encryption, and (2) watermarking by securing biometric images and templates. In this paper, we utilise a watermarking technology to improve the template security in biometric authentication. According to, two modalities such as, iris and hand vein is taken to preserve the characteristics of liveliness and permanency. Our proposed technique for embedding of iris data to hand vein images using watermarking technology to improve template protection in biometric recognition is done based on the following steps, i) pre-processing of iris and hand vein images, ii) iris template extraction, iii) Vein extraction, iv) Embedding of iris pattern to vein images based on region of interest, v) Storing embedded images. In the recognition phase, iris pattern is extracted from the embedded image and then, matching is done with query images. The final decision of authentication is done based on the product rule-based score level fusion. The implementation is done using MATLAB and the performance of the technique is analysed with FAR, FRR and accuracy.

This paper presents an innovative contactless palm print and palm vein recognition system. We design a hand sensor that could capture the palm print and palm vein image using low-resolution web camera. Both the visible and infrared images can be captured at the same time, and we do not need specialized infrared sensor to image the vein pattern. The design of the device is simple and low-cost. The subject can be shielded completely from the complication of undergoing two separate acquiring processes. We allow subjects to position their hands freely above the sensor and they can move their hands during the acquisition process. In order to obtain clear image of the palm vascular pattern, we propose a novel image enhancement technique called local-ridge-enhancement (LRE). The proposed method removes illumination error while keeping good contrast between the print/vein pattern and the background image. Besides, we introduce a simple yet robust directional coding technique to encode the palm print and palm vein features in bit string representation. The bit string representation offers speedy template matching and enables more effective template storage and retrieval. The scores output by the palm print and palm vein experts are fused using Support Vector Machine. The fusion of these features yields promising result for practical implementation.

Palm vein recognition has emerged as a promising biometric technology, yet its development remains constrained by the scarcity of large-scale publicly available datasets. Several methods of palm vein image generation have been proposed to address this issue. These methods usually focus on the anatomical realism of palm vein patterns, but overlook the biophysical correlation between identities and vein patterns, particularly in simulating identity-specific vein contrast. To tackle this limitation, we propose a novel biophysics-driven synthesis method. Our method constructs a 3D palm vascular tree via established modeling method. Then, a projection model is proposed to map the 3D tree into 2D space to derive palm vein patterns. The projection model is based on skin spectral absorption and simulates the natural attenuation of light passing through the skin using a layer integration method. For different identities, we sample different skin parameters, resulting in varying degrees of attenuation. This method effectively simulates the variation in vein contrast across different identities. Furthermore, we introduce a conditional diffusion model that uses the projected patterns as identity conditions to generate palm vein images. To the best of our knowledge, this is the first palm vein generation method based on the diffusion model. Experimental results demonstrate that our method not only outperforms existing methods, but also enables a recognition model trained on our synthetic data to achieve superior performance compared to a model trained on real-world data at a scale of 2,000 IDs under an open-set protocol with a TAR@FAR=1:1 of 1e-4.

Palm Vein Identification (PVI) is a modern biometric security technique used for enhancing security and&#13;\nauthentication systems. The key characteristics of palm vein patterns include its uniqueness to each individual, its&#13;\nunforgettability, non-intrusiveness and its ability for disallowing unauthorized persons. However, the extracted&#13;\nfeatures from the palm vein patterns are huge with high redundancy. In this paper, we propose a combined model of&#13;\ntwo-Dimensional Discrete Wavelet Transform, Principal Component Analysis (PCA), and Particle Swarm&#13;\nOptimization (PSO) (2D-DWTPP) that feeds wrapper model with an optimal subset of features to enhance the&#13;\nprediction accuracy of -palm vein patterns. The 2D-DWT extract features from palm vein images, using the PCA to&#13;\nreduce the redundancy in palm vein features. The system has been trained to select high recognition features based on&#13;\nthe wrapper model. The proposed system uses four classifiers as an objective function to determine PVI which include&#13;\nSupport Vector Machine (SVM), K Nearest Neighbor (KNN), Decision Tree (DT) and Naïve Bayes (NB). The&#13;\nempirical results proved that the proposed model has the best results with SVM. Moreover, our proposed 2D-DWTPP&#13;\nmodel has been evaluated and the results show remarkable efficiency in comparison with AlexNet and other classifiers&#13;\nwithout feature selection. Experimentally, the proposed model has better accuracy as reflected by 98.65% whereas&#13;\nAlexNet has 63.5% accuracy and the classifier without feature selection process has 78.79% accuracy.

Vascular pattern based biometric recognition is gaining more and more attention, with a trend towards contactless acquisition. An important requirement for conducting research in vascular pattern recognition are available datasets. These datasets can be established using a suitable biometric capturing device. A sophisticated capturing device design is important for good image quality and, furthermore, at a decent recognition rate. We propose a novel contactless capturing device design, including technical details of its individual parts. Our capturing device is suitable for finger and hand vein image acquisition and is able to acquire palmar finger vein images using light transmission as well as palmar hand vein images using reflected light. An experimental evaluation using several well-established vein recognition schemes on a dataset acquired with the proposed capturing device confirms its good image quality and competitive recognition performance. This challenging dataset, which is one of the first publicly available contactless finger and hand vein datasets, is published as well.

The demand on devices and systems empowered by biometric verification and identification mechanisms has been increasing in recent years as they have become a significant part of our lives. Palm vein biometric is an emerging technology that has drawn considerable attention from researchers and scientists over the last decade. In the present work, a novel feature extraction methodology named GPWLD combing the Gabor features with positional Weber's local descriptor (PWLD) features is proposed. WLD is a highly representative micro-pattern descriptor that performs well against noise and illumination changes in images. However, it lacks the ability to capture the vein pattern features at different orientations. Moreover, its descriptor packs the local information content into a single histogram that does not take the spatial locality of micro-patterns into consideration. To solve these two issues, firstly, the palm vein image is passed through Gabor filter with different orientations to capture the salient rotational features found in the output feature maps. Secondly, the spatiality is achieved by uniformly dividing each feature map into several blocks. Next, Weber's law feature descriptor (WLD) histogram is computed for each block in every feature map. Finally, these histograms are concatenated to compose the final feature vector. Due to the high dimensionality of the final feature vector, Principal component analysis (PCA) algorithm is utilized for feature size reduction. In the classification stage, a deep neural network (DNN) comprising an optimized stacked autoencoder (SAE) with Bayesian optimization and a softmax layer is used. Optimization of the SAE is carried out by using Bayesian optimization to find the optimal SAE structure and the options of the training algorithm. The Experimental results verify the discriminative power of the extracted features and the accuracy of the proposed DNN. For both Identification and verification experiments, the proposed method attains higher identification rate and lower EER than state-of-the-art methods.

In this work, a novel near-infrared (NIR) scanner called MultiVein is designed. The self-produced device can efficiently collect near-infrared dorsal hand vein (DHV) images in real time. It has two attractive characteristics. First, MultiVein can work in two imaging modes that can switch freely, i.e. the transmission mode and the reflection mode. Second, MultiVein can adjust flexibly illumination intensity of the infrared backlight LED array. Balanced illumination and high adaptability of ambient conditions can be achieved by this operation. The quality of the images acquired by MultiVein was evaluated qualitatively and quantitatively, as well as comparisons with competitive instruments were conducted. The experimental results demonstrated that MultiVein can acquire reflection or transmission NIR dorsal hand vein images with high quality and effective acquisition. It was believed that MultiVein has promising application in DHV biometrics and health care.KeywordsDorsal hand vein biometricsNear-infrared imagingDual imaging modeReflection modeTransmission mode

Aiming at the problem that using maximum inscribed circle algorithm to extract the region of interest (ROI) in palm vein image is retrieved and recorded too much, an improved maximum inscribed circle algorithm is proposed. First of all, the palm vein image was done by background separation and smoothing filter preprocessing. Secondly, the grids were added for the preprocessed image, the selection range of the center was determined by the assistant of grids, the initial radius and the variable value of radius were defined. Then, changed the radius in variable size, recorded all inscribed circles. Finally, compared and obtained the maximum inscribed circle of the palm vein image. The circle was the ROI which was needed. The experimental results show that the execution time of the new algorithm is reduced by 10.7 ms, 10.2 ms, 11.3 ms and 10.8 ms under four groups of samples respectively. Therefore, the new algorithm effectively improves the efficiency of the maximum inscribed circle algorithm.