Hand Vein Patterns Research Articles

Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods.

Various techniques for dorsal hand vein (DHV) pattern extraction have been introduced using small datasets with poor and inconsistent segmentation. This work compared manual segmentation with our proposed hybrid automatic segmentation method (HHM) for this classification problem. Manual segmentation involved selecting a region-of-interest (ROI) in images from the Bosphorus dataset to generate ground truth data. The HHM combined histogram equalization and morphological and thresholding-based algorithms to localize veins from hand images. The data were divided into training, validation, and testing sets with an 8:1:1 ratio before training AlexNet. We considered three image augmentation strategies to enlarge our training sets. The best training hyperparameters were found using the manually segmented dataset. We obtained a good test accuracy (91.5%) using the model trained with manually segmented images. The HHM method showed slightly inferior performance (76.5%). Considerable improvement was observed in the test accuracy of the model trained with the inclusion of automatically segmented and augmented images (84%), with low false acceptance and false rejection rates (0.00035% and 0.095%, respectively). A comparison with past studies further demonstrated the competitiveness of our technique. Our technique can be feasible for extracting the ROI in DHV images. This strategy provides higher consistency and greater efficiency than the manual approach.

Gender-Specific Characteristics for Hand-Vein Biometric Recognition: Analysis and Exploitation

In recent years, vein-based biometric recognition has received ever-increasing attention from both academia and industry, due to the advantages it offers over traditional biometric traits such as fingerprint, iris, and face. Nonetheless, some issues related to the use of vein biometrics still need to be investigated and understood. Specifically, in this study, we speculate about the gender-related variations in vein patterns, and their effects on biometric verification performance. An analysis on the feasibility of recognizing male and female subjects depending on their hand-vein patterns, and on the level of similarity characterizing the biometric templates extracted from male and female populations, are here carried out considering three different databases. Specifically, the public VERA dataset, containing samples of palm-vein patterns, and two datasets containing images of finger-vein patterns, i.e., the UTFVP public database, and an in-house dataset collected with an on-the-move contactless modality, are here considered. The obtained experimental results show that the approach here proposed to perform gender recognition allows to reach an accuracy up to 95.83% on the public finger-vein UTFVP dataset, and to outperform the current state-of-the-art on the public palm-vein VERA dataset, with accuracy at 93.55%. It is also shown that vein-based biometric systems can benefit from the exploitation of information regarding the gender of the considered subjects, with achievable recognition rates that can be significantly improved by designing a biometric verification system relying on gender-specific models for extracting the employed discriminative templates.

Boosting Unsupervised Dorsal Hand Vein Segmentation with U-Net Variants

The identification of vascular network structures is one of the key fields of research in medical imaging. The segmentation of dorsal hand vein patterns form NIR images is not only the basis for reliable biometric identification, but would also provide a significant tool in assisting medical intervention. Precise vein extraction would help medical workers to exactly determine the needle entry point to efficiently gain intravenous access for different clinical purposes, such as intravenous therapy, parenteral nutrition, blood analysis and so on. It would also eliminate repeated attempts at needle pricks and even facilitate an automatic injection procedure in the near future. In this paper, we present a combination of unsupervised and supervised dorsal hand vein segmentation from near-infrared images in the NCUT database. This method is convenient due to the lack of expert annotations of publicly available vein image databases. The novelty of our work is the automatic extraction of the veins in two phases. First, a geometrical approach identifies tubular structures corresponding to veins in the image. This step is considered gross segmentation and provides labels (Label I) for the second CNN-based segmentation phase. We visually observe that different CNNs obtain better segmentation on the test set. This is the reason for building an ensemble segmentor based on majority voting by nine different network architectures (U-Net, U-Net++ and U-Net3+, all trained with BCE, Dice and focal losses). The segmentation result of the ensemble is considered the second label (Label II). In our opinion, the new Label II is a better annotation of the NCUT database than the Label I obtained in the first step. The efficiency of computer vision algorithms based on artificial intelligence algorithms is determined by the quality and quantity of the labeled data used. Furthermore, we prove this statement by training ResNet–UNet in the same manner with the two different label sets. In our experiments, the Dice scores, sensitivity and specificity with ResNet–UNet trained on Label II are superior to the same classifier trained on Label I. The measured Dice scores of ResNet–UNet on the test set increase from 90.65% to 95.11%. It is worth mentioning that this article is one of very few in the domain of dorsal hand vein segmentation; moreover, it presents a general pipeline that may be applied for different medical image segmentation purposes.

Dorsal Hand Vein Identification using Transfer Learning from AlexNet

Dorsal hand vein pattern is a highly secured biometric system that has been used in many applications due to its non-contact attributes. Prior studies focused on investigation of different deep networks for hand vein classification task using different training parameters. It is the aim of this study to propose the use of systematic fine-tuning system for identifying the best parameters value for enhanced model learning efficiency. In this study, pre-trained AlexNet was trained using Bosphorus hand vein database for identification of 100 users. The experiments were carried out using original images, and preprocessed (cropped) images for comparison. The testing accuracies of these datasets were compared following tuning of training parameters, namely trainingand testing split ratio, number of epochs, mini-batch size and initial learning rate. It was observed that the testing accuracy of the model trained using cropped images is higher than that using the original images. The model from preprocessed dataset shows a good testing accuracy of 96 % using a split ratio of 90:10, epoch 50, mini-batch-size of 10 and an initial learning rate of 0.0001. The performance of our trained model is more superior than many reported results in the field. In future, the performance of this classification system may be further enhanced with automatic search of parameters for improved model training efficiency.

On the intra-subject similarity of hand vein patterns in biometric recognition

In these years, biometric recognition based on hand vein patterns is receiving an always increasing attention from both industry and academia, thanks to the advantages it offers with respect to conventional approaches, such as those relying on fingerprint, iris, or face. Nevertheless, there are still several properties of vein traits that need to be investigated and well understood. In this paper, we here analyze the level of similarity, evaluated in terms of recognition rate of a biometric system, of vein patterns in the fingers, palms, and dorsa of a person’s left and right hands. In other words, we analyze whether a subject, enrolled using vein patterns, either finger-vein, palm-vein, dorsal-vein, from one hand, can be recognized using the corresponding patterns from the other hand. Our investigation is conducted using deep-learning-based feature extraction approaches, three different vein modalities, and four different databases. The obtained experimental results show that corresponding fingers, palms, and dorsal regions from different hands of the same subject show more resemblance with respect to the traits from the same hand of different persons. Furthermore, our findings point out that similarities among vein patterns in corresponding fingers could be used for recognition purposes, while this still cannot be applied to palm and dorsum vein patterns.

Dorsal Hand Vein Image Enhancement Using Fusion of CLAHE and Fuzzy Adaptive Gamma.

Enhancement of captured hand vein images is essential for a number of purposes, such as accurate biometric identification and ease of medical intravenous access. This paper presents an improved hand vein image enhancement technique based on weighted average fusion of contrast limited adaptive histogram equalization (CLAHE) and fuzzy adaptive gamma (FAG). The proposed technique is applied using three stages. Firstly, grey level intensities with CLAHE are locally applied to image pixels for contrast enhancement. Secondly, the grey level intensities are then globally transformed into membership planes and modified with FAG operator for the same purposes. Finally, the resultant images from CLAHE and FAG are fused using improved weighted averaging methods for clearer vein patterns. Then, matched filter with first-order derivative Gaussian (MF-FODG) is employed to segment vein patterns. The proposed technique was tested on self-acquired dorsal hand vein images as well as images from the SUAS databases. The performance of the proposed technique is compared with various other image enhancement techniques based on mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM). The proposed enhancement technique’s impact on the segmentation process has also been evaluated using sensitivity, accuracy, and dice coefficient. The experimental results show that the proposed enhancement technique can significantly enhance the hand vein patterns and improve the detection of dorsal hand veins.

Robust Vein Recognition against Rotation Using Kernel Sparse Representation

Sparse representation due to advantages such as noise-resistant and, having a strong mathematical theory, has been noticed as a powerful tool in recent decades. In this paper, using the sparse representation, kernel trick, and a different technique of the Region of Interest (ROI) extraction which we had presented in our previous work, a new and robust method against rotation is introduced for dorsal hand vein recognition. In this method, to select the ROI, by changing the length and angle of the sides, undesirable effects of hand rotation during taking images have largely been neutralized. So, depending on the amount of hand rotation, ROI in each image will be different in size and shape. On the other hand, because of the same direction distribution on the dorsal hand vein patterns, we have used the kernel trick on sparse representation to classification. As a result, most samples with different classes but the same direction distribution will be classified properly. Using these two techniques, lead to introduce an effective method against hand rotation, for dorsal hand vein recognition. Increases of 2.26% in the recognition rate is observed for the proposed method when compared to the three conventional SRC-based algorithms and three classification methods based sparse coding that used dictionary learning.

Improvement of confusion matrix for Hand Vein Recognition Based On Deep- Learning multi-classifier Decisions

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.

Inverse Biometrics: Generating Vascular Images From Binary Templates

In this work, we investigate the possibility of generating grayscale images of finger and hand vein patterns from their corresponding binary templates. This would allow us to determine the invertibility of vascular templates, which has implications in biometric security and privacy. The transformation from binary features to a gray-scale image is accomplished using a Pix2Pix Convolutional Neural Network (CNN). The reversibility of 6 different types of binary features is evaluated using this CNN. Further, a number of experiments are conducted using 8 distinct finger vein datasets and 3 hand vein datasets. Results indicate that (a) it is possible to reconstruct the considered vascular images from their binary templates; (b) the reconstructed images can be used for biometric recognition purposes; (c) the CNN trained on one dataset can be successfully used for reconstructing images in a different dataset (cross-dataset reconstruction); and (d) the images reconstructed from one set of features can be successfully used to extract a different set of features for biometric recognition (cross-feature-set generalization). The results of this research further underscore the need for properly securing biometric templates, even if they are of binary nature.

Dorsal Hand Veins Based Biometric Identification System Using Deep Learning

Identification systems have become biometric-based, especially with the increase in the performance rates of machine learning methods. Biometric identification systems offer a high level of security by using reliable, difficult-to-change parameters. In this thesis, a biometric identification system is proposed using dorsal hand vein patterns. The relevant system has been tested on the sample dataset in the literature. The number of data were increased by adding noisy data to the data set used. The classification was made on the preprocessed images using SVM, ANN, LDA + KNN, and CNN methods. It has been determined that the highest identification accuracy is achieved when CNN is used, and CNN method provides higher performance compared to other methods. With the proposed identification system, after multiple runs, an average accuracy of 99.64% is achieved with the CNN machine learning method.

Dorsal hand vein authentication system using artificial neural network

&lt;span&gt;Biometric feature authentication technology had been developed and implemented for the security access system. However, the known biometric features such as fingerprint, face and iris pattern failed to provide ideal security. Dorsal hand vein is the features beneath the skin which makes it not easily be duplicated and forged. It was expected to be used in biometric authentication technology to achieve an ideal accuracy with the uniqueness of its characteristics. In this paper, 240 images of 80 users were obtained from Bosphorus Hand Vein Database. The images were then pre-processed by cropping ROI, mean filtering, CLAHE enhancing and histogram equalizing. The ROI was then segmented by implementing binarization. The local binary pattern (LBP) features were then extracted from the segmented ROI. The extracted features were sent to an artificial neural network (ANN) for the classification of the images. The training result shows that the LBP features and ANN can recognize the dorsal hand vein pattern quite well with 99.86% accuracy. The ANN was then utilized in the MATLAB GUI program for testing 100 images (80 trained images of 80 users and 20 untrained images of 20 users) from the Bosphorus Hand Vein Database. The results revealed 100% accuracy in their matching result.&lt;/span&gt;

A new dorsal hand vein authentication system based on fractal dimension box counting method

Dorsal hand vein pattern is a physiological feature that can distinguish and define one person from another. Feature extraction from images is considered as the most important step in biometric systems. In this study, a fractal technique, which is both an advanced and a complex method, is proposed for feature extraction from the images of hand vessel pattern. In recent years, this approach has been widely used as an active research area in image processing. Therefore, fractal size based tissue analysis method, which is calculated by box counting method, which is a new technique in determining the tissue properties of the dorsal hand vein, has been applied. Experimental findings on the dorsal vein databases of Bosphorous and SUAS show that our proposed method yields promising and optimistic results compared to other known techniques.

ADVIT: Using the potentials of deep representations incorporated with grid-based features of dorsum vein patterns for human identification

The identification of an individual is one of the main applications of forensic science, used in legal settings for deciding cases in courts of law. Different methods have been developed for the identification of a person, including fingerprints, DNA profiling, retina scan, facial features and many others. The reliable and accurate identification mainly relies upon substantial variability of structures and features of evidence corresponding to reference material. During the last decade, human identification through hand vein patterns has been focused in various studies and shown promising results. However, most of the reported methods require extensive human efforts for manual feature calculation. Herein, we propose a novel identification tool namely ADVIT for the identification of humans based on their dorsum veins pattern. The samples of the dorsum of the right or left hand were collected from 50 participants in the form of images. Initially, images were preprocessed and noise (in terms of hair on the skin and other details) was removed. Later on, the vein skeleton was extracted from the preprocessed images and a binary image of veins pattern (veins in the foreground and every other detail as background) was generated. Two different types of the feature were computed and based on these features, three different experiments were performed and the evaluation metrics were computed. Merging of hand-crafted grid-based features and deep representation from RESNET-50 showed maximum results in terms of Sensitivity (0.8803), Specificity (0.8890), Precision (0.8849), False Positive Rate (0.1074), Accuracy (0.8861), F1 Score (0.8817), and MCC (0.7636). These results depicted that the model is accurate and sensitive for identification through dorsum veins pattern. The proposed model can aid forensic scientists to identify perpetrator using hand images. ADVIT is freely available at (http://zeetu.org/advit.html).

A simple approach to use hand vein patterns as a tool for identification

IntroductionIn a case of child pornography, only the dorsum of the offender's hand was clearly visible. After identification of a suspect, the question arose of whether and how it is possible to identify or exclude the suspect as perpetrator according to the morphology of the hand vein pattern. Material and methodsA simple approach to use the hand vein pattern in crime suspects as a tool for identification was tested. In this study, the hand vein patterns of 30 study participants were analysed from conventional frames on videography. A standardised grid system consisting of six lines and four sectors was applied on the dorsum of the hands. Vein branchings within the sectors and line crossings of the veins were counted, leading to a total of 11 variables for each hand. ResultsA positive identification of each of the 30 test participants was possible for each hand when taking only the first five variables into account. A random overlapping prediction was obtained by statistically simulating hand vein patterns of different numbers of persons using this sample. Considering the hand vein frequencies in this sample, the results indicate that the chance for two persons having the same pattern is smaller than 1:1000. ConclusionsIt can be concluded that the introduced grid system approach can be an appropriate simple and non-costly tool for the analysis of the pattern of hand veins for identification purposes.

Vein-Based Biometric Verification Using Densely-Connected Convolutional Autoencoder

In this letter, we propose a vein-based biometric verification system relying on deep learning. A novel approach consisting of a convolutional neural network (CNN), trained in a supervised manner, cascaded with an auto-encoder, trained in an unsupervised way, is here exploited. In more detail, a novel densely-connected convolutional autoencoder is here used on top of backbone CNNs. This architecture aims at increasing the discriminative capability of the features generated from hand vein patterns. Experimental tests on finger, palm, and dorsal veins show that the proposed approach leads to an improvement of the recognition rates with respect to the use of the sole CNNs for feature extraction. The achieved performance are superior to the current state of the art in vein biometric verification.

Intelligent technique for human authentication using hand vein

In this paper, we propose a new intelligent technique to authenticate human using dorsal hand vein (DHV) pattern. Recently, authentication was adopted by smart hospitals in many countries as an intelligent tool for patient identification to prevent insurance and can connect the patient with his or her medical record securely. In this paper we developed an image analysis technique to extract region of interest (ROI) from DHV image. After extracting ROI we design a sequence of preprocessing steps to improve hand vein images using Median filter, Wiener filter and contrast limited adaptive histogram equalisation (CLAHE) to enhance hand vein image. Our smart technique is based on the following intelligent algorithms, namely; principal component analysis (PCA) algorithm for feature extraction and k-nearest neighbours (K-NN) classifier for matching operation. This technique has been applied on the Bosphorus Hand Vein Database. The experimental results show that the result of (CRR) is 91.2%.

An Efficient Dorsal Hand Vein Recognition Security System Using Arduino And Gsm Technology

With the onset of maximum power, modest figuring and more prominent unpredictability, biometric verification has turned out to be conceivable at each scale in light of its more secure nature and furthermore easy to use conduct. Compare to other biometrics, vein biometric is a decent verification characteristic among others. The dorsal hand vein recognition is an emerging biometric procedure which is utilized for verification purposes in many applications. In this work pre-processing is done by median filter and region of interest such as veins separated from the muscles and bones through adaptive K-means clustering algorithm.The proposed method extracts the dorsal hand vein pattern features by using LBP and Repeated Line Tracking algorithm.Finally recognition and authentication is done using Artificial Neural Network. Arduino and GSM technology is used in this work to set security preference for the particular user.In order to validate the proposed work , a total of 480 images of dorsal hand veins is involved in this work. In a comparison with four existingverification algorithms, the proposed method achieves thehighest accuracy with lowest error rate.

Zernike moment invariants for hand vein pattern description from raw biometric data

We propose an invariant description method based on Zernike moments to classify hand vein patterns from raw infrared (IR) images. Orthogonal moments provide linearly independent descriptors and are invariant to affine transformations, such as translation, rotation, and scaling. A mathematical expression is given to derive a set of moment invariants. The obtained features have all the properties of moment invariants with the additional feature of image contrast invariance. For dorsal hand vein pattern acquisition, an IR imaging system is implemented. Also, a public database is used for a palm vein recognition task. A correct rate classification (CRC) above 99.9% is achieved using a set of rotation, scale, and intensity Zernike moment invariants. Additionally, multilayer perceptron and K-nearest neighbors are used as classifiers having as input data the Zernike normalized moments. A discriminative feature evaluation of the image moments allows the reduction of the number of descriptors while maintaining a high classification rate of 99%. The efficiency of the moment descriptors is evaluated in terms of accuracy and reduced computational cost by (a) avoiding the necessity of a preprocessing stage and (b) reducing the feature vector dimension. Experimental results show that Zernike moment invariants are able to achieve hand vein recognition without image preprocessing or image normalization with respect to change of size, rotation, and intensity.

Ultrasound Systems for Biometric Recognition.

Biometric recognition systems are finding applications in more and more civilian fields because they proved to be reliable and accurate. Among the other technologies, ultrasound has the main merit of acquiring 3D images, which allows it to provide more distinctive features and gives it a high resistance to spoof attacks. This work reviews main research activities devoted to the study and development of ultrasound sensors and systems for biometric recognition purposes. Several transducer technologies and different ultrasound techniques have been experimented on for imaging biometric characteristics like fingerprints, hand vein pattern, palmprint, and hand geometry. In the paper, basic concepts on ultrasound imaging techniques and technologies are briefly recalled and, subsequently, research studies are classified according to the kind of technique used for collecting the ultrasound image. Overall, the overview demonstrates that ultrasound may compete with other technologies in the expanding market of biometrics, as the different commercial fingerprint sensors integrated in portable electronic devices like smartphones or tablets demonstrate.

Hand Vein Authentication System based on Information Sets

have been used from ages for authentication purpose but the key advantages of vein pattern that is hand vein pattern are 100 times more unique than other physiological are attracting many researchers. In this paper we have proposed an efficient authentication system using vein pattern. The concept of information sets has been used for feature extraction so as to have control over the local information. The information set based features has already been implemented on biometrics like ear and face. Till now these features have not been implemented on vein pattern based verification systems hence we have explored their suitability on veins. All the experiments have been carried out on vein database developed at IIT, DELHI .We have used Hanman classifier to check the accuracy of extracted features. Then we have optimized the Hanman classifier performance by using genetic algorithm. The optimized results shows that the proposed system in 99% efficient. We have also compared Hanman classifier accuracy with accuracy obtained from k- nearest neighbor and SVM classifier. Keywordsvein, Biometrics, Information sets, Fuzzy sets, Hanman