煤矿入井人员唯一性检测技术研究

通过分析掌纹、指纹、虹膜、人脸、步态、声纹等生物特征识别技术的特点以及煤矿现场对入井人员生物特征的影响,指出虹膜识别、人脸识别、步态识别、声纹识别适用于煤矿入井人员唯一性检测;提出了一种基于人员定位和生物特征识别的煤矿入井人员唯一性检测技术方案,将生物特征识别技术嵌入人员定位系统,利用人员定位识别卡实现识别卡数量及人员身份的唯一性检测;指出煤矿入井人员唯一性检测技术的研究关键点是严重污染人脸的识别算法、对设备遮挡情况下人员步态图像的采集及对混入人员语音信号的煤矿现场噪声消除算法。     

生物特征模板保护综述

近年来频繁出现的网络安全事件使得网络安全上升到国家战略的高度,可靠的互联网身份认证是互联网应用的关键环节,基于生物特征的身份认证技术可以保证用户物理身份和数字身份的一致性而受到关注。为了保护生物特征模板的安全性和隐私性,近年来提出了生物特征模板保护研究。首先分析了生物特征识别系统面临的安全问题,对生物特征模板保护技术在生物特征识别系统中的定位和具体概念进行了介绍。其次,基于国内外的研究工作,归纳了各类生物特征模板保护技术的特点、效果和应用范围,包括生物哈希、不可逆变换法、密钥绑定以及密钥生成等;阐述了每类方法的主要思想、代表性工作以及主要特点,并从隐私性、实用性、敏感性、安全性和认证性能方面对其中的典型方法进行了总结对比;进一步介绍了多生物特征融合的模板保护算法,主要包括多模态生物特征和单模态生物特征的不同特征融合的模板保护方法。最后,对生物特征模板保护存在的问题进行了分析,并对进一步的研究方向进行了展望。     

An algorithm for finger-vein segmentation based on modified repeated line tracking

Abstract

Image segmentation is an important step for finger-vein identification technique. However, it is difficult to extract precise details of the image because of the irregular noise and shades around the finger-vein. The repeated line tracking algorithm achieves good segmentation performance for low quality images of finger-vein, but it has some drawbacks such as low robustness and efficiency. In this paper, a modified repeated line tracking algorithm is proposed for image segmentation of finger-vein. Firstly, we propose a segmentation method called threshold image to execute rough segmentation and obtain binary and skeleton image of finger-vein. Secondly, the width of finger-vein is estimated based on the binary and skeleton image. The parameters are revised according to the width. Then, the modified repeated line tracking algorithm is executed to figure out the locus space of finger-vein based on the revised parameters. Finally, processing results are obtained by using Otsu algorithm which executes exact segmentation on the locus space. Experiments show that the proposed algorithm is more robust and efficient than traditional repeated line tracking algorithm.     

基于智能卡指纹认证系统

文中介绍基于智能卡的指纹认证系统,它结合指纹识别技术与智能卡技术各自的优点,提供更高的安全性和更好的实用性.     

采用信号处理方法的生物特征密钥产生器

人的生物特征如手写签名、指纹、虹膜等已被成功应用于各种身份认证和识别系统中。提出了一种基于生物特征的密钥产生器,具有安全、成本低和使用方便等的优点。算法采用信号处理方法来消除生物特征中的相似性,并在Windows智能手机上进行了验证,实验证明了算法的有效性和安全性。     

A data reduction scheme for active authentication of legitimate smartphone owner using informative apps ranking

Smartphones are vulnerable to fraudulent use despite having strong authentication mechanisms. Active authentication based on behavioral biometrics is a solution to protect the privacy of data in smart devices. Machine-learning-based frameworks are effective for active authentication. However, the success of any machine-learning-based techniques depends highly on the relevancy of the data in hand for training. In addition, the training time should be very efficient. Keeping in view both issues, we’ve explored a novel fraudulent user detection method based solely on the app usage patterns of legitimate users. We hypothesized that every user has a unique pattern hidden in his/her usage of apps. Motivated by this observation, we’ve designed a way to obtain training data, which can be used by any machine learning model for effective authentication. To achieve better accuracy with reduced training time, we removed data instances related to any specific user from the training samples which did not contain any apps from the user-specific priority list. An information theoretic app ranking scheme was used to prepare a user-targeted apps priority list. Predictability of each instance related to a candidate app was calculated by using a knockout approach. Finally, a weighted rank was calculated for each app specific to every user. Instances with low ranked apps were removed to derive the reduced training set. Two datasets as well as seven classifiers for experimentation revealed that our reduced training data significantly lowered the prediction error rates in the context of classifying the legitimate user of a smartphone.     

Performance‐enhanced modified self‐organising map for iris data classification

Biometric systems are widely used in applications such as forensics and military. Biometric authentication is a challenging and complex task. These biometric systems must be accurate for practical applications. In this era of artificial intelligence, artificial neural network‐based classifiers are widely used in biometric‐based systems. However, most of the artificial neural network‐based classifiers are less accurate and computationally complex. In this work, two modified self‐organising map (SOM) networks are proposed for iris image classification to improve the performance measures. Particle swarm optimization technique is used in the training process of conventional SOM. The experiments are carried out with conventional and modified classifiers. The proposed modified classifiers provide better performance than the conventional SOM classifier.     

语音识别在倒谱领域中的验证

为了验证语音识别系统的鲁棒性,对其过程进行监控,设置了一个假想情景:用一个人来模仿别人的讲话,通过混合转换装置,在倒谱领域中,声音转换能够对音频信号进行计算和修改,通过实验得知身份识别系统的鲁棒性是可以实现的.     

基于指纹改进的模糊金库算法

该文提出基于指纹改进的模糊金库算法可以同时解决保护短密钥时系统安全性不足,和攻击者通过交叉比对可捕获生物特征模板的问题.该方案采用两个多项式和两个指纹,每个指纹对应一个多项式.每个多项式系数由密钥、随机数和校验值3部分组成,以此解决密钥的长度影响系统安全性的问题.提取同一用户的两个不同指纹的细节点,再分别从两个指纹细节点集合中随机地选择部分细节点共同构成生物特征模板.随机选取的细节点不同,它们共同组成的生物特征模板就不同,由此产生的模糊金库就不同,这就实现了模糊金库的可撤销性.