Palmvein Recognition Using Block-Based WLD Histogram of Gabor Feature Maps and Deep Neural Network With Bayesian Optimization

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.

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.

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%.

Palm vein biometrics is contactless identification through vascular vein patterns. The paper presents a new dataset of 500 palm vein images from 100 individuals in 5 conditions (normal, hot, cold, dusty, and lotion-applied). In contrast to current benchmarks, the dataset directly simulates environmental and physiological variations. It compares three feature-extraction pipelines (Kumar Gabor, IUWT-SAD and Maximum Curvature) to a proposed multi-feature ensemble SVM. The proposed SVM uses HOG, LBP and Gabor features. In all cases, the ensemble achieves a mean EER of 4.0 %, TAR FAR=10-3 =72.3%, and AUC= 0.963, which is on par with Kumar Gabor (EER 8.8%), MC (EER 13.1%), and IUWT-SAD (EER 16.4%). Performance is consistent in response to temperature changes. There is only slight performance deterioration in the presence of surface contaminants (dust, lotion). Calibration analysis indicates low error (ECE &lt; 0.02, Brier &lt; 0.03). A throughput of up to 12 images per second is achieved with the proposed feature pipeline. The results demonstrate that ensemble fusion is highly effective for condition-resilient palm vein recognition. The new dataset offers a good reference point for estimating real-world resilience beyond laboratory tests.

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 hand 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 hand vein 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 hand vein 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. The results are also compared to our earlier technique and the results shows that the proposed technique has achieved better results having GAR of 94% without noise and 88% with noise.

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 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.

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.

A simple AXI based convolution architecture for deep learning is presented. Input feature maps and kernel weights are stored in P K∗K memory blocks and convolution is done from output feature map 0 to M-1, and inside a feature map, output is generated in raster scan order. Data from P input feature maps are summed in parallel during convolution. It is possible to provide P K∗K input feature map data, P K∗K weights and the bias for the input and output feature maps being processed by manipulating the read addresses and read data alignment. Dual buffers are used to perform convolution for output feature map while DMA write for previous final output feature map is in progress. Correct operation was verified by comparing RTL simulation and C program run results. This method provides over 2,000 speed-up compared to pure software method and with flow control between DMA and convolution, much less memory can be used. This architecture can be used for convolution acceleration for moderate deep learning applications on embedded systems.

Contactless biometric technologies such as palm vein recognition have gained more relevance in the present and immediate future due to the COVID-19 pandemic. Since certain soft biometrics like gender and age can generate variations in the visualization of palm vein patterns, these soft traits can reduce the penetration rate on large-scale databases for mass individual recognition. Due to the limited availability of public databases, few works report on the existing approaches to gender and age classification through vein pattern images. Moreover, soft biometric classification commonly faces the problem of imbalanced data class distributions, representing a limitation of the reported approaches. This paper introduces weighted extreme learning machine (W-ELM) models for gender and age classification based on palm vein images to address imbalanced data problems, improving the classification performance. The highlights of our proposal are that it avoids using a feature extraction process and can incorporate a weight matrix in optimizing the ELM model by exploiting the imbalanced nature of the data, which guarantees its application in realistic scenarios. In addition, we evaluate a new class distribution for soft biometrics on the VERA dataset and a new multi-label scheme identifying gender and age simultaneously. The experimental results demonstrate that both evaluated W-ELM models outperform previous existing approaches and a novel CNN-based method in terms of the accuracy and G-mean metrics, achieving accuracies of 98.91% and 99.53% for gender classification on VERA and PolyU, respectively. In more challenging scenarios for age and gender–age classifications on the VERA dataset, the proposed method reaches accuracies of 97.05% and 96.91%, respectively. The multi-label classification results suggest that further studies can be conducted on multi-task ELM for palm vein recognition.

Every human being has its own uniqueness among humans that other good physical form or characteristic trait. Biometrics is the science that can recognize a part of an individual. Therefore, biometric identification is one of the ways that is done to identify the identity of the person. Palm vein biometric is one who lately attracted many researchers and industry because it has several advantages over other physical characteristics such as fingerprint, iris and face. Palm vein has internal features making it difficult undermined, modified and simulated with fake palms. In this study, design and implementation will do a recognition system through the venous vessels. Systems that were built capable of taking images of the Palm veins, detect the presence of venous vessels are then able to tell based on the database. This is done after the process of extraction, processing and manufacturing characteristics against the image of Palm vein vessels. The method used is the phase and learning vector quantization symmetri. Feature extraction using phase symmetri. phase symmetri applied by calculating the amplitude and phase of the signal frequency of the image of the vein which represents detailed information detailed variant of an image of the vein. While learning vector quantization is used for grouping the exclusive result of extraction. This research resulted in the classification with accuracy reaching 94%.

Today, the novel COVID-19 pandemic has taken a significant toll on countries worldwide and impacted the lives and well-being of people across nations. Measures need to be taken to slow down the spread of such viruses, which can be reduced by taking proper precautions to avoid unnecessary contact and incurring hygienic habits. People can become infected by touching infected objects or surfaces, then touching their eyes, nose or mouth. We need proper and hygienic authentication systems for granting access to authorized users at various places such as companies, universities, banks, etc. Today there are multiple biometric systems that can serve this purpose, but to maintain hygiene we need systems that are contactless in nature thus to reduce spread of infections through touch. The palm vein pattern is distinctive biometric identity of individuals that is also a safe and reliable biometric authentication technique. The major advantage of palm vein biometric that we consider in our proposed system is that this kind of authentication can be done in a contactless way. Here, we propose an authentication system that uses palm vein pattern biometric to authenticate users in a contactless way. We have also added the functionalities of temperature detection and blood oxygen level detection to this system. These health symptoms are the major symptoms of various fatal virus infections such as the coronavirus. By checking the users for these symptoms before granting access, we can further limit the spread of infections and also help detect the infected patients. The proposed system does this in a contactless way.

Palm vein recognition using image processing is a cutting-edge biometric authentication technology that identifies individuals based on the unique patterns of veins in their palms.This method leverages near-infrared light to capture images of the veins beneath the skin's surface, which are then processed using advanced image processing techniques.The extracted vein patterns are analysed and compared against stored templates to authenticate individuals with a high level of accuracy and security.Unlike other biometric modalities, such as fingerprints or iris recognition, palm vein patterns are internal and difficult to replicate, making this approach highly resistant to forgery and spoofing.This paper provides an overview of palm vein recognition, highlighting its key characteristics, benefits, and applications across various industries including security, healthcare, finance, and personal devices.Additionally, it discusses the role of image processing in enhancing the accuracy and reliability of palm vein recognition systems, as well as future research directions aimed at further improving this technology.Overall, palm vein recognition offers a promising solution for secure and convenient authentication in diverse environments, with the potential to revolutionize the way individuals are identified and verified.