Frequency of Basic Types of Dorsal Hand Vein Patterns in the Slovak Population

Biometric identification is an emerging technology that can solve security problems in our networked society. A reliable and robust personal verification approach using dorsal hand vein patterns is proposed in this paper. The characteristic of the approach needs less computational and memory requirements and has a higher recognition accuracy. In our work, the near-infrared charge-coupled device (CCD) camera is adopted as an input device for capturing dorsal hand vein images, it has the advantages of the low-cost and noncontact imaging. In the proposed approach, two finger-peaks are automatically selected as the datum points to define the region of interest (ROI) in the dorsal hand vein images. The modified two-directional two-dimensional principal component analysis, which performs an alternate two-dimensional PCA (2DPCA) in the column direction of images in the 2DPCA subspace, is proposed to exploit the correlation of vein features inside the ROI between images. The major advantage of the proposed method is that it requires fewer coefficients for efficient dorsal hand vein image representation and recognition. The experimental results on our large dorsal hand vein database show that the presented schema achieves promising performance (false reject rate: 0.97% and false acceptance rate: 0.05%) and is feasible for dorsal hand vein recognition.

Hand vein patterns are among the biometric traits being investigated today for identification purposes, attracting interest from both the research community and industry. A reliable and robust personal verification approach using dorsal hand vein patterns is presented in this paper. This approach needs less computational and memory requirements and has a higher recognition accuracy than similar methods. In our work, a near-infrared charge-coupled device camera is adopted as an input device for capturing dorsal hand vein images, due to its advantages of the low-cost and non-contact imaging. In the proposed approach, two finger-peaks are automatically selected to define the region of interest in the dorsal hand vein images. In order to obtain effective pattern of dorsal hand vein vascular, we proposed an innovative and robust adaptive Gabor filter method to extract the dorsal hand vein patterns and encode the vein features in bit string representation. The bit string representation, called VeinCode, offers speedy template matching and enables more effective template storage and retrieval. The similarity of two VeinCodes is measured by normalised Hamming distance. A total of 6160 dorsal hand vein images were collected from 308 persons to verify the validity of the proposed dorsal hand vein recognition approach. High accuracies (>99%) have been obtained by the proposed method, and the speed of the method (responding time <0·8 s) is rapid enough for real-time recognition. Experimental results demonstrate that our proposed approach is feasible and effective for dorsal hand vein recognition.

— Actually, hand vein biometrics is a recent technology that offers system for identification /authentication, it ranks among the best biometric modality by the results developed. Just like any recognition system this has four steps: the acquisition, enhancement, feature extraction and classification. This paper present the enhancement’s step of the SAB11 Data Base followed by new adaptive feature extraction method for the dorsal hand vein biometrics; which is the discrete wavelet transform. Keywords — dorsal hand vein patterns, wavelet transform, feature extraction . I. INTRODUCTION Hand veins Biometrics have received considerable attentions in recent years. With vein pattern offers one of the best results by their stability and unicity, still more, the biometrics of the hand veins are not expensive for realized and very convenient to use by users. Good recognition should have a good classification and a good classification should be above a perfect feature extraction phase this is where lies the strength of the biometric system, our work is focused on the dorsal hand veins feature extraction step, but the question asked is which method used to ensures a better feature extraction? In this paper the hand veins pattern are shown in gray level image. The main objective of this work is to provide a method which allows feature extraction of veins pattern from low quality images. II. PRIOR WORK They are several works about feature extraction of hand veins pattern, among them there is the Gabor filter, the Hough transform, discrete Curvelet transform, triangulation of minutiae...etc. most of his method are preceded by a preprocessing step where in the Gabor filter [1] and the Hough transform [2] they use the Median filter, Wiener in Gabor [1] and SIFT method [4], the Mexican hat in triangulation minutiae [5].The table 1 summarizes most of this works.

People are depending on information very much and it is becoming essential to carry out different activities every day. The degree of security in many situations becoming critical and need to be paid attention because of increase of crime rate with sophisticated technology. To increase the privacy and safety of information care must be taken to protect the information. Individual authentication is achieved using Biometric traits in different fields. As biometric authentication, there are methods using finger print, iris, and voice and so on. For the disadvantages of other biometric recognition methods, dorsal hand vein pattern has emerged as new biometric because the vascular structure is unique to every individual. The vein is the inner features of body and can't be fabricated and they are lasting. So it can be used for identification of individuals in highly secured areas. In this project, a new approach is proposed for extracting critical features from the dorsal hand vein pattern using the concepts of Walsh transform and Euclidean distance similarity measure. The proposed methodology has been tested on a dataset of 100 dorsal hand vein images and the experimental results are found to be promising.

Fusion of palm-phalanges print with palmprint and dorsal hand vein

Even though various dorsal hand vein pattern extraction techniques have been proposed for biometric identification, there remains considerable room for performance. This paper describes dorsal hand vein recognition using statistical and Gray Level Co-occurrence Matrix (GLCM) based features extraction techniques and artificial neural networks (ANN). For this purpose, 240 images of 80 users were obtained from Bosphorus Hand Vein Database. The images were first pre-processed by cropping region of interest (ROI), before the application of mean filtering, contrast enhancing and histogram equalizing. The ROI was then segmented by implementation of binarization method. The statistical and GLCM features were then extracted from the segmented ROI. These extracted features were sent to ANN for classification of the images. The training result shows that the proposed technique is able to recognize dorsal hand vein pattern with with considerably high accuracy of 99.32%.

In this revolutionized and digital world, the increasing need of security to protect individuals and information has led to a rise in developing biometric systems over traditional security systems such as pincode and password. Finding more reliable, practical and more acceptable biometrics and techniques are attracting the attention of researchers. Recently, hand vein pattern biometrics has gained increasing interest from both research communities and industries. Researchers are exploiting the different biometric phases by applying existing techniques or devising new ones to develop enhanced biometric systems. Up to now, most researchers have thinned the dorsal hand vein pattern and apply corresponding techniques for feature representation and matching. However, not many techniques have been explored with relation to considering the whole hand vein image. In this research work, local binary pattern, which is a powerful technique for representing texture description of an image, have been applied on dorsal hand vein images. This method outperforms existing vein representation techniques by having a recognition rate of 98.4% on a database of more than 1000 images. In addition, this proposed method has no effect on rotated images, which is desirable in any biometric security system.

With the increasing needs in security systems, hand vein recognition is reliable as one of the important solutions for biometrics-based identification systems. This work presents an effective approach for dorsal hand vein recognition by analysing vein patterns. The methodology involves extraction of dorsal hand vein features based on Gaussian filter. In order to obtain effective pattern of dorsal hand vein vascular, we propose an innovative and robust Gaussian directional filter method to extract the dorsal hand vein patterns and to encode the vein features in binary code. A new coding scheme, namely Gaussian directional binary code, is then proposed for dorsal hand vein recognition. To evaluate the efficacy of the proposed approach, the normalised Hamming distance used in recognition is adopted. A total of 5120 dorsal hand vein images were collected from 256 persons to verify the validity of the proposed dorsal hand vein recognition approach. High accuracies (>99%) and low equal error rate (0·95%) have been obtained by the proposed method. Experimental results demonstrate that our proposed approach is feasible and effective for dorsal hand vein recognition.

Reliable personal authentication is a critical and vital obligation to the security in all the real-world applications. Nevertheless, biometric features are effectively used for the personal authentication, in some cases the criminal impersonation is an easy task. The reproducible attack in fingerprint system and cost prohibitive nature of iris and facial based system limit the vast implementation of the same for personal authentication. Hence, considering the cases of increasing identity theft, there is more reason than ever to ensure the reliable and cost-effective personal authentication. The vein biometric identification system has been gaining increased attention in recent years. Anatomically, the shape of the vein pattern at the dorsal hand is unique for each even for identical twins and remains stable for a period. The objective of this work is to conduct a feasibility analysis of unique ID generation using dorsal hand vein pattern as the biometric authentication system for preventing identity theft of the valuable documents. Images of the vein pattern from the dorsal side of the hand are tried to capture using a regular type Smartphone camera. Vein pattern thus captured is used to extract the unique features. Numerical values generated from such unique feature are encrypted and used to create a unique ID. This paper discusses about the method for the dorsal hand vein pattern capturing, its feature extraction, conversion to the unique ID and its feasibility to integrate to the valuable documents.

Hand vein patterns are among the biometric traits being investigated today for identification purposes, attracting interest from both the research community and industry. This paper presents a multimodal system that combines hand-palm vein and hand-dorsal vein biometrics information at the score level. The palm and dorsal veins are considered as texture samples being automatically extracted from the user's hand image. A 2D Gabor filter is employed for texture feature extraction. For matching, the newly acquired biometric samples are compared with those stored in the system database, at the enrolment stage. The metric used is based on the Hamming distance. A palm and dorsal vein database is built using the proposed acquisition system. This paper proposes a novel multimodal system that combines palm and dorsal vein information at the score level, with the experimental results showing that with the proposed system a much lower Equal Error Rate (EER) can be achieved, in comparison to existing unimodal systems.

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.

So far many biometric systems such as fingerprint, palm print and iris have been developed for several years. Nowadays, many researchers are interested in developing new and more efficient biometric systems by using alternative features. In line with this, a newer characteristic that is dorsal hand vein patterns are used to identify an individual because its uniqueness, reliability, permanence and difficulty to forge. To develop a dorsal hand vein biometric security system, the hand vein images are first captured using an appropriate setup. Several preprocessing techniques are then applied to obtain a thinned version of the image. One challenging phase in biometric security system is the feature extraction phase. In this work, three feature extraction and representation techniques namely Hough lines transform, Pixel by Pixel Method and Directional Coding Method have been explored and implemented. These techniques are applied on 500 images obtained from 100 individuals of different age. For matching, Mahalanobis Distance and Correlation Percentage have been used. From the experimental results, it was deduced that Pixel by Pixel Method proved to be the best feature extraction technique with a False Rejection Rate (FRR) of 0.03%.

ObjectivesVarious 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.MethodsManual 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.ResultsWe 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.ConclusionsOur technique can be feasible for extracting the ROI in DHV images. This strategy provides higher consistency and greater efficiency than the manual approach.

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

The hand vein pattern is a biometric feature in which the actual pattern is the shape of the vein network and its characteristics are the vein features. This paper investigates a new approach for dorsal hand vein pattern identification from grey level dorsal hand vein information. In this study Gabor filter quadrature pair is employed to compute locally in a window for every pixel position to extract the phase information. The phases of six frequency coefficients are quantized and it is used to form a descriptor code for the local region. These local descriptors are decorrelated using whitening transformation and a histogram is generated for every pixel which describes the local pattern. Experiments are evaluated on North China University of Technology dorsal hand vein image dataset with minimum distance classifier and the results are analyzed for recognition rate, run time and equal error rate. The proposed method gives 100 per cent recognition rate and 1 per cent EER for fusion of both left and right hands. Defence Science Journal, 2014, 64(2), pp. 159-167. DOI: http://dx.doi.org/10.14429/dsj.64.4659