生物特征识别技术研究及应用

生物特征识别技术作为一种身份识别的手段,具有独特的优势,对信息安全具有重要意义,近年来已逐渐成为国际上的研究热点。本文介绍了生物特征的概念及多种常见生物特征识别技术,对不同的识别方法的原理、特征及性能做了较详细的分析与评价。     

生物特征识别技术及相关问题综述

生物特征识别技术利用人的生物特征信息,自动实现对个人身份的识别和认证。本文综述了目前已有的八种生物特征识别技术,比较其在特征提取方法以及实际应用等方面的不同之处。同时,还讨论了生物特征识别系统自身的安全性问题,以及其他相关问题。     

生物特征识别技术的现状与发展趋势概述

生物特征识别技术是利用人的生理特征或行为特征,来进行个人身份的鉴定。文章论述了现有得各种生物特征识别技术的原理,特征的优缺点,介绍生物特征识别技术的发展趋势。     

多生物特征识别平台的设计与实现

针对目前生物特征识别研究大多基于特定特征而难以集成其它特征的现状，提出了整合多种生物特征识别技术的通用解决方案．通过对生物特征识别研究的需求进行分析，抽取了生物特征识别平台的基本逻辑模型．建立了易于扩展、可复用的生物特征识别平台框架THBio，同时在平台中进一步整合了生物特征识别评测厦多生物特征融合的功能，为提高生物特征识别技术的研究水平提供了可能．     

基于生物特征的身份识别技术

就目前的技术发展情况来看，指纹识别与虹膜识别将成为未来身份认证的主流技术，传统的密码认证将被逐渐淘汰。[编者按]     

生物特征识别技术的实现原理与前景分析

生物特征识别技术作为身份鉴别的重要手段之一,已经越来越多地走进了我们的世界。小到个人办公笔记本电脑的启动、公司员工出勤的考核,大到机场、海关的通行、国家信息安全的保密等,生物特征识别技术在各个领域都发挥着无可比拟的作用。     

生物特征安全技术

在美国纽约的肯尼迪国际机场,来自Iridian Technologies的虹膜识别系统可以对进入国际到港停机坪的人员进行身份鉴别和出入控制。机场雇员的身份卡必须与实时虹膜扫描的结果相符才能找开通往4号候机厅的大门。如果只持有身份卡而虹膜扫描与记录不符,大门也不会被打开。因为如果仅仅依靠身份卡或者密码来开门的话,一旦身份卡和密码被盗就会严重威胁候机厅的安全。     

生物特征识别技术发展与应用综述

生物特征识别技术是一种利用人体独一无二的生物特征进行身份识别的技术。介绍了当前生物特征识别技术的工作模式和发展现状,指出了生物特征识别技术的发展趋势,并对当前生物特征识别过程中面对的安全性进行了论述。     

多生物特征识别

生物特征的识别技术是一门利用人类特有的个体特征来验证个人身份的科学,它与传统的基于口令和个人身份号码的识别方法相比更加可靠．本文先对各种不同生物特征识别技术作了简要介绍,然后,介绍了多生物特征识别技术及其工作原理和方法,对生物特征识别技术的应用前景和发展方向进行了分析．     

生物特征识别技术概述

一、前言 现代社会．随着城市规模的不断扩大和交通网络的日益发达,城市流动人口数量越来越大,如何对大规模流动性人口进行简单、有效的身份识别与认证成为重要的问题,而金融、国家安全、司法、电子商务、电子政务等领域的安全可靠的自动身份识别与认证更具重要性。     

基于掌纹图象分析的身份识别系统

研究了人体掌纹的生物测定学特征,掌纹图象的边界提取,建立了具有唯一性和可识别性的掌纹图象特征空间,设计了用于掌纹图象识别的ＡＲＴ２神经网络,并在此基础上建立了快速、可靠性高的新型身份识别系统。     

Biometric dispersion matcher versus LDA

This paper compares the recently developed biometric dispersion matcher (BDM) with the classical linear discriminant analysis (LDA) for biometric pattern recognition. BDM is extended to the BDM with simultaneously diagonalization (BDMSD) of the covariance matrices and, from a theoretical point of view, it is demonstrated that the feature selection of LDA and BDMSD are equivalent. However, LDA uses the between-class scatter matrix (S_B) only for feature selection and BDMSD also uses it for classification. This implies a set of advantages. Mainly the BDMSD offers better generalization capability for classifying samples of users that have not been used for training the classifier. Experimental results show that BDM and BDMSD outperform LDA in face recognition and hand-geometry recognition. These two cases correspond to very different situations: number of samples greater than their dimensionality (hand-geometry) and number of samples similar to their dimensionality (face recognition).     

A machine-vision system for iris recognition

This paper describes a prototype system for personnel verification based on automated iris recognition. The motivation for this endevour stems from the observation that the human iris provides a particularly interesting structure on which to base a technology for noninvasive biometric measurement. In particular, it is known in the biomedical community that irises are as distinct as fingerprints or patterns of retinal blood vessels. Further, since the iris is an overt body, its appearance is amenable to remote examination with the aid of a machine-vision system. The body of this paper details the design and operation of such a system. Also presented are the results of an empirical study in which the system exhibits flawless performance in the evaluation of 520 iris images.     

Combine crossing matching scores with conventional matching scores for bimodal biometrics and face and palmprint recognition experiments

In this paper, we define for the first time the crossing matching score of two biometrics traits and combine it with the conventional matching scores to perform personal authentication. The proposed method is very suitable for the bimodal biometrics systems with two similar biometrics traits such as the system with visible light and infrared face images and the system with palm images captured at two bands. The proposed method first runs for the first and biometrics traits, respectively. For each of these two biometrics traits, the proposed method calculates the matching scores between the testing sample and each training sample. The matching scores generated from the first and second traits are referred to as the first and second matching scores, respectively. Second, the proposed method calculates the crossing matching scores, i.e. the matching scores between the testing sample of the second biometrics trait and the training samples of the first biometrics trait. Finally, we use a weighted fusion scheme to combine the first, second and crossing matching scores for personal authentication.     

Database, protocols and tools for evaluating score-level fusion algorithms in biometric authentication

Fusing the scores of several biometric systems is a very promising approach to improve the overall system's accuracy. Despite many works in the literature, it is surprising that there is no coordinated effort in making a benchmark database available. It should be noted that fusion in this context consists not only of multimodal fusion, but also intramodal fusion, i.e., fusing systems using the same biometric modality but different features, or same features but using different classifiers. Building baseline systems from scratch often prevents researchers from putting more efforts in understanding the fusion problem. This paper describes a database of scores taken from experiments carried out on the XM2VTS face and speaker verification database. It then proposes several fusion protocols and provides some state-of-the-art tools to evaluate the fusion performance.     

生物特征信息处理开发包BITK及其应用平台

由于人类固有的生物特征能用来进行可靠的身份认证和识别,近10年来生物特征识别已经成为研究热点.为了对生物特征识别研究提供软件支持,设计研发了生物特征信息处理开发包 (Biometric Information Processing Toolkit,BITK).BITK是一个C 软件开发包.它以非线性数据流模型来组织整体计算流程,提供了一个可扩展、可重用的框架来整合生物特征识别领域的算法和数据结构.BITK还设计了一个精干的可视化框架以及一个管理各种生物特征采集仪的一致性框架.此外,在BITK基础上研发了生物特征信息处理平台 (BITK-based Application Platform,BITKAPP).该平台通过灵活的plug-in架构和友好的用户界面,充分发挥BITK的功能,并降低了BITK的使用门槛.在实际应用中,BITK和BITKAPP向研究人员提供了一套高效率的研究、实验及开发手段,并且作为支撑平台完成了第一届生物特征识别竞赛(the 1st Biometric Verification Competition)的多模态数据采集任务.     

Biometric dispersion matcher

In this paper we propose a new classifier called a dispersion matcher. Our proposal is especially well adapted to those scenarios where a large number of classes and a small number of samples per class are available for training. This is the situation of biometric systems where just three to five measures per person are acquired during enrollment. This is just the opposite situation of other pattern recognition applications where a small number of classes and a large amount of training samples are available, such as handwritten digit recognition (10 classes) for ZIP code identification.The dispersion matcher trains a quadratic discriminant classifier to solve the dichotomy “Do these two feature vectors belong to the same person?”. In this way, we solve an important set of topics: (a) we can classify an open world problem and we do not need to train the model again if a new user is added, (b) we find a natural solution for feature selection, (c) experimental results with a priori threshold provides good results.We evaluate the proposed system with hand-geometry and face recognition problems (identification and verification). In hand geometry, we get a minimum detection cost function (DCF) for verification of 0.21% and a maximum identification rate of 99.1%, which compares favorably with other state-of-the-art methods. In face verification we achieve 5.59% DCF and 92.77% identification rate, which also compares favorably with the literature.     

Online finger-knuckle-print verification for personal authentication

Biometric based personal authentication is an effective method for automatically recognizing, with a high confidence, a person's identity. By observing that the texture pattern produced by bending the finger knuckle is highly distinctive, in this paper we present a new biometric authentication system using finger-knuckle-print (FKP) imaging. A specific data acquisition device is constructed to capture the FKP images, and then an efficient FKP recognition algorithm is presented to process the acquired data in real time. The local convex direction map of the FKP image is extracted based on which a local coordinate system is established to align the images and a region of interest is cropped for feature extraction. For matching two FKPs, a feature extraction scheme, which combines orientation and magnitude information extracted by Gabor filtering is proposed. An FKP database, which consists of 7920 images from 660 different fingers, is established to verify the efficacy of the proposed system and promising results are obtained. Compared with the other existing finger-back surface based biometric systems, the proposed FKP system achieves much higher recognition rate and it works in real time. It provides a practical solution to finger-back surface based biometric systems and has great potentials for commercial applications.     

Comparison and combination of iris matchers for reliable personal authentication

The personal identification approaches using iris images are receiving increasing attention in the biometrics literature. Several methods have been presented in the literature and those based on the phase encoding of texture information are suggested to be the most promising. However, there has not been any attempt to combine these approaches to achieve further improvement in the performance. This paper presents a comparative study of the performance from the iris authentication using Log-Gabor, Haar wavelet, DCT and FFT based features. Our experimental results suggest that the performance from the Haar wavelet and Log-Gabor filter based phase encoding is the most promising among all the four approaches considered in this work. Therefore, the combination of these two matchers is most promising, both in terms of performance and the computational complexity. Our experimental results from the all 411 users (CASIA v3) and 224 users (IITD v1) database illustrate significant improvement in the performance which is not possible with either of these approaches individually.