Biometric Person Identification Using Near-infrared Hand-dorsa Vein Images

Biometric recognition is becoming more and more important with the increasing demand for security, and more usable with the improvement of computer vision as well as pattern recognition technologies. Hand vein patterns have been recognised as a good biometric measure for personal identification due to many excellent characteristics, such as uniqueness and stability, as well as difficulty to copy or forge. This thesis covers all the research and development aspects of a biometric person identification system based on near-infrared hand-dorsa vein images. Firstly, the design and realisation of an optimised vein image capture device is presented. In order to maximise the quality of the captured images with relatively low cost, the infrared illumination and imaging theory are discussed. Then a database containing 2040 images from 102 individuals, which were captured by this device, is introduced. Secondly, image analysis and the customised image pre-processing methods are discussed. The consistency of the database images is evaluated using mean squared error (MSE) and peak signal-to-noise ratio (PSNR). Geometrical pre-processing, including shearing correction and region of interest (ROI) extraction, is introduced to improve image consistency. Image noise is evaluated using total variance (TV) values. Grey-level pre-processing, including grey-level normalisation, filtering and adaptive histogram equalisation are applied to enhance vein patterns. Thirdly, a gradient-based image segmentation algorithm is compared with popular algorithms in references like Niblack and Threshold Image algorithm to demonstrate its effectiveness in vein pattern extraction. Post-processing methods including morphological filtering and thinning are also presented. Fourthly, feature extraction and recognition methods are investigated, with several new approaches based on keypoints and local binary patterns (LBP) proposed. Through comprehensive comparison with other approaches based on structure and texture features as well as performance evaluation using the database created with 2040 images, the proposed approach based on multi-scale partition LBP is shown to provide the best recognition performance with an identification rate of nearly 99%. Finally, the whole hand-dorsa vein identification system is presented with a user interface for administration of user information and for person identification

Handbook of Vascular 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

Biometric Systems

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

An Analytical Survey on Vein Pattern Recognition

Biometric is term of science to identify a person identity using their physiological features. Currently, vein pattern recognition has attracted the attention of the technology and industry all over the world. A vein is network of blood vessels under the skin of an individual. The vascular pattern is different for every person in the same part or region of the body. It is stable till very long age. As the veins are underneath the skin it is very difficult for intruder or forger to copy the feature. This uniqueness and strong immunity from intruders make it more potent biometric system which avails us secure features for individual identity verification. This paper involves the description of vein pattern recognition, its requirement and its importance in biometric system. Different feature extraction algorithms are reviewed as independent component analysis, principal component analysis method. For classification in vein pattern recognition we have reviewed support vector machine and neural network techniques. Parameters are described based on which results are computed like true positive, false positive, true negative, false negative, accuracy and precision

Human Identification Using Finger Vein and Texture Matching

No Abstrac

A pilot study on discriminative power of features of superficial venous pattern in the hand

The goal of the project is to develop an automatic way to identify, represent the superficial vasculature of the back hand and investigate its discriminative power as biometric feature. A prototype of a system that extracts the superficial venous pattern of infrared images of back hands will be described. Enhancement algorithms are used to solve the lack of contrast of the infrared images. To trace the veins, a vessel tracking technique is applied, obtaining binary masks of the superficial venous tree. Successively, a method to estimate the blood vessels calibre, length, the location and angles of vessel junctions, will be presented. The discriminative power of these features will be studied, independently and simultaneously, considering two features vector. Pattern matching of two vasculature maps will be performed, to investigate the uniqueness of the vessel network / L’obiettivo del progetto è di sviluppare un metodo automatico per identificare e rappresentare la rete vascolare superficiale presente nel dorso della mano ed investigare sul suo potere discriminativo come caratteristica biometrica. Un prototipo di sistema che estrae l’albero superficiale delle vene da immagini infrarosse del dorso della mano sarà descritto. Algoritmi per il miglioramento del contrasto delle immagini infrarosse saranno applicati. Per tracciare le vene, una tecnica di tracking verrà utilizzata per ottenere una maschera binaria della rete vascolare. Successivamente, un metodo per stimare il calibro e la lunghezza dei vasi sanguigni, la posizione e gli angoli delle giunzioni sarà trattato. Il potere discriminativo delle precedenti caratteristiche verrà studiato ed una tecnica di pattern matching di due modelli vascolari sarà presentata per verificare l’unicità di quest

A Novel Encryption Method for Dorsal Hand Vein Images on a Microcomputer

In this paper, a Lorenz-like chaotic system was developed to encrypt the dorsal hand patterns on a microcomputer. First, the dorsal hand vein images were taken from the subjects via an infrared camera. These were subjected to two different processes called contrast enhancement and segmentation of vein regions. Second, the pre- and post-processed images were encrypted with a new encryption algorithm in the microcomputer environment. For the encryption process, random numbers were generated by the chaotic system. These random numbers were subjected to NIST-800-22 test which is the most widely accepted statistical test suite. The speeded up robust feature (SURF) matching algorithm was utilized in the initial condition sensitivity analysis of the encrypted images. The results of the analysis have shown that the proposed encryption algorithm can be used in identification and verification systems. The encrypted images were analyzed with histogram, correlation, entropy, pixel change rate (NPCR), initial condition sensitivity, data loss, and noise attacks which are frequently used for security analyses in the literature. In addition, the images were analyzed after noise attacks by means of peak signal-to-noise ratio (PSNR), mean square error (MSE), and the structural similarity index (SSIM) tests. It has been shown that the dorsal hand vein images can be used in identification systems safely with the help of the proposed method on microcomputers.This work was supported by the Qatar National-LibraryScopu

Multispectral iris recognition analysis: Techniques and evaluation

This thesis explores the benefits of using multispectral iris information acquired using a narrow-band multispectral imaging system. Commercial iris recognition systems typically sense the iridal reflection pertaining to the near-infrared (IR) range of the electromagnetic spectrum. While near-infrared imaging does give a very reasonable image of the iris texture, it only exploits a narrow band of spectral information. By incorporating other wavelength ranges (infrared, red, green, blue) in iris recognition systems, the reflectance and absorbance properties of the iris tissue can be exploited to enhance recognition performance. Furthermore, the impact of eye color on iris matching performance can be determined. In this work, a multispectral iris image acquisition system was assembled in order to procure data from human subjects. Multispectral images pertaining to 70 different eyes (35 subjects) were acquired using this setup. Three different iris localization algorithms were developed in order to isolate the iris information from the acquired images. While the first technique relied on the evidence presented by a single spectral channel (viz., near-infrared), the other two techniques exploited the information represented in multiple channels. Experimental results confirm the benefits of utilizing multiple channel information for iris segmentation. Next, an image enhancement technique using the CIE L*a*b* histogram equalization method was designed to improve the quality of the multispectral images. Further, a novel encoding method based on normalized pixel intensities was developed to represent the segmented iris images. The proposed encoding algorithm, when used in conjunction with the traditional texture-based scheme, was observed to result in very good matching performance. The work also explored the matching interoperability of iris images across multiple channels. This thesis clearly asserts the benefits of multispectral iris processing, and provides a foundation for further research in this topic