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As one of the most promising biometric technologies, vein pattern recognition (VPR) is quickly taking root around the world and may soon dominate applications where people focus is key. Among the reasons for VPR‘s growing acceptance and use: it is more accurate than many other biometric methods, it offers greater resistance to spoofing, it focuses
This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers.
With the increasing demand for security worldwide, biometric recognition is becoming more and more important; and with the improvements in computer vision and pattern recognition technologies it is becoming more usable as well. Hand vein pattern is a biometric feature in which the actual pattern has the shape of the vein network and its characteristics are the vein features. The main objective of this project work is to develop a personal recognition system based on hand dorsal vein pattern with a high recognition rate. Hand vein biometrics offer higher security: It is easy to acquire the hand vein image and very hard to forge the data compared to more established biometric verification methods, with a comparable or improved recognition rate. Weber local binary pattern (WLBP) consists of two components named differential excitation and Local Binary Pattern (LBP). The differential excitation extracts perception features by Weber's law and the LBP describe the local features. By computing the two components the differential excitation image and the LBP image are extracted. WLBP histogram is constructed using these two images. HOG is a technique, which counts occurrences of gradient orientation in localized portions of an image. WLBP and HOG features are fused together and it is used for classifying the images. The next section of the work computes the binary code string for every pixel of a given image by means of natural image statistical filters. The code value of the pixel considers the local descriptor of an image intensity values in the pixel’s neighborhood. The value of each bit binary code is calculated by binarizing the response of linear filter with a threshold at 0. Each bit is related with different filters and the preferred length of the binary code decides the number of filters used. The most important method for image representation and analysis is the spatial frequency transformation, which can be represented in terms of magnitude and phase. Phase is highly invulnerable to noise and contrast distortions and it is an important feature desirable in image processing. In the final section of the work, the twelve bit code of the feature vector from 0-4095 is obtained using local Gaussian phase quantization and local Gabor phase quantization. These codes for all image pixel neighborhoods are collected into histogram bins, which can be used for hand vein image classification.
This book constitutes the refereed proceedings of the 9th Chinese Conference on Biometric Recognition, CCBR 2014, held in Shenyang, China, in November 2014. The 60 revised full papers presented were carefully reviewed and selected from among 90 submissions. The papers focus on face, fingerprint and palmprint, vein biometrics, iris and ocular biometrics, behavioral biometrics, application and system of biometrics, multi-biometrics and information fusion, other biometric recognition and processing.
This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers.
This book constitutes the refereed proceedings of the Third International Conference on Biometrics, ICB 2009, held in Alghero, Italy, June 2-5, 2009. The 36 revised full papers and 93 revised poster papers presented were carefully reviewed and selected from 250 submissions. Biometric criteria covered by the papers are assigned to face, speech, fingerprint and palmprint, multibiometrics and security, gait, iris, and other biometrics. In addition there are 4 papers on challenges and competitions that currently are under way, thus presenting an overview on the evaluation of biometrics.
This open access handbook provides the first comprehensive overview of biometrics exploiting the shape of human blood vessels for biometric recognition, i.e. vascular biometrics, including finger vein recognition, hand/palm vein recognition, retina recognition, and sclera recognition. After an introductory chapter summarizing the state of the art in and availability of commercial systems and open datasets/open source software, individual chapters focus on specific aspects of one of the biometric modalities, including questions of usability, security, and privacy. The book features contributions from both academia and major industrial manufacturers. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
A palm vein biometrics system is essentially a pattern recognition system that operates by acquiring an image of the palm veins, extracting a feature set from the image, and comparing this feature set against the template saved in a database. Unlike other biometric technologies such as fingerprints or face recognition, the palm vein scanner works by capturing the images of the vein patterns that are beneath the skin of the palm. Thus, palm vein based biometrics are more secure than fingerprints and palm prints. Moreover, the palm vein scanner captures the images of vein patterns in a contactless manner, which makes it more sterile and hygienic to use. However, the palm prints are also available in the Near Infrared (NIR) illumination, around 760nm wavelength. In some multispectral palmprint databases, the palmprint and palm vein images are both available in the same image. In this thesis, the features of both palm vein and palmprint are used for recognition. The process of palm vein recognition can be divided into several stages: image acquisition, pre-processing, feature extraction, matching and decision making. In order to build a reliable and accurate system, the unchangeable features of the palm prints/veins must be efficiently extracted from the original image. The difficulty of this problem, combined with the development of hyperspectral imaging techniques, has motivated the research presented in this thesis. Line or linear prints/veins detection have played an important part in palm prints/vein recognition. These techniques have been reviewed and explored. However, the vulnerability to the change of palm position and ambient illumination, together with low accuracy, encouraged researchers to find more stable algorithms. Fourier transform, wavelet transform and other frequency domain transforms are more robust and stable in palm prints/veins recognition. Due to the sparsity of the palm image, which is usually composed of some prints and veins, it's convenient to only transform the linear features to the frequency domain. Thus, in this thesis, the Curvelet Transform is introduced to extract the curve-like features from the palm print/vein images for accurate and sparse representation. The palm image is decomposed to several scales of coefficients, while in each scale the coefficients represent different features of the palm image. This technique can reduce the storage of the palm image to several hundred bytes and improve the accuracy as well. In order to increase the recognition accuracy, a combination of several biometrics features should be considered as well. The Curvelet Transform is good at extracting the curve-like features for accurate and sparse representation while the Gabor Filter can preserve local orientations. A combining scheme is proposed to utilise both of the two recognition methods at the same time with single near-infrared palm image. This combining scheme improves the recognition accuracy a lot, compared with techniques with solely Curvelet Transform or Gabor Filter. The experimental results demonstrate the effectiveness of the proposed method. As we can get information more easily and more accurately, the problem of information redundancy emerges, especially in hyperspectral imaging. As the number of palm images in the database increases, more effective methods of categorizing palm veins and fast matching algorithms should be developed. In hyperspectral palmprint recognition systems, it's not convenient to use exhaustive search for the optimal band selection and combination. A bands selection scheme is developed at the pre-processing stage.
This book includes reviewed papers by international scholars from the 2020 International Conference on Pattern Recognition and Artificial Intelligence (held online). The papers have been expanded to provide more details specifically for the book. It is geared to promote ongoing interest and understanding about pattern recognition and artificial intelligence. Like the previous book in the series, this book covers a range of topics and illustrates potential areas where pattern recognition and artificial intelligence can be applied. It highlights, for example, how pattern recognition and artificial intelligence can be used to classify, predict, detect and help promote further discoveries related to credit scores, criminal news, national elections, license plates, gender, personality characteristics, health, and more.Chapters include works centred on medical and financial applications as well as topics related to handwriting analysis and text processing, internet security, image analysis, database creation, neural networks and deep learning. While the book is geared to promote interest from the general public, it may also be of interest to graduate students and researchers in the field.
Because of the accelerating progress in biometrics research and the latest nation-state threats to security, this book's publication is not only timely but also much needed. This volume contains seventeen peer-reviewed chapters reporting the state of the art in biometrics research: security issues, signature verification, fingerprint identification, wrist vascular biometrics, ear detection, face detection and identification (including a new survey of face recognition), person re-identification, electrocardiogram (ECT) recognition, and several multi-modal systems. This book will be a valuable resource for graduate students, engineers, and researchers interested in understanding and investigating this important field of study.