Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers

Simple and secured access to networked home appliances via internet using SSL, BioHashing and single Authentication Server

This thesis describes a web-based application that will enable users to access their networked home appliances over the Internet in an easy, secured, accessible and cost effective manner, using the user's iris image only for authentication. As Internet is increasingly gaining significance and popularity in our daily lives, various home networking technologies also started gaining importance from consumers, which helped in facilitating interoperability, sharing of services and exchange of information between different electronic devices at home. As a result, the demand to be able to access home appliances or security cameras over the Internet gradually grew. In this research, we propose an efficient, secured, low-cost and user-friendly method to access networked home appliances over the Internet, providing strong, well integrated, three levels of security to the whole application and user data. According to our design, the user's iris data after hashing (using BioHashing) is sent through a secure communication channel utilizing Secure Sockets Layer v-3.0. The deterministic feature sequence from the iris image is extracted using 1D log-Gabor filters and while performing BioHashing, the orthonormalization of the pseudorandom number is implemented employing Gram-Schmidt orthonormalization algorithm. In addition to this protected data transfer mechanism, we propose the design of an Authentication Server that can be shared among multiple homes, allowing numerous users to access their home appliances in a trouble-free and secured manner. It can also bring down the cost of commercial realization of this endeavor and increase its accessibility without compromising on system security. We demonstrate that the recognition efficiency of this system is computationally effective with equal error rate (EER) of 0% and 6.75% (average) in two separate conditions on CASIA 1 and CASIA 2 iris image datasets

Biometric privacy protection : guidelines and technologies

Compared with traditional techniques used to establish the identity of a person, biometric systems offer a greater confidence level that the authenticated individual is not impersonated by someone else. However, it is necessary to consider different privacy and security aspects in order to prevent possible thefts and misuses of biometric data. The effective protection of the privacy must encompass different aspects, such as the perceived and real risks pertaining to the users, the specificity of the application, the adoption of correct policies, and data protection methods as well. This chapter focuses on the most important privacy issues related to the use of biometrics, it presents actual guidelines for the implementation of privacy-protective biometric systems, and proposes a discussion of the methods for the protection of biometric data

Biometric identity verification using on-line & off-line signature verification

Biometrics is the utilization of biological characteristics (face, iris, fingerprint) or behavioral traits (signature, voice) for identity verification of an individual. Biometric authentication is gaining popularity as a more trustable alternative to password-based security systems as it is relatively hard to be forgotten, stolen, or guessed. Signature is a behavioral biometric: it is not based on the physical properties, such as fingerprint or face, of the individual, but behavioral ones. As such, one's signature may change over time and it is not nearly as unique or difficult to forge as iris patterns or fingerprints, however signature's widespread acceptance by the public, make it more suitable for certain lower-security authentication needs. Signature verification is split into two according to the available data in the input. Off-line signature verification takes as input the image of a signature and is useful in automatic verification of signatures found on bank checks and documents. On-line signature verification uses signatures that are captured by pressure-sensitive tablets and could be used in real time applications like credit card transactions or resource accesses. In this work we present two complete systems for on-line and off-line signature verification. During registration to either of the systems the user has to submit a number of reference signatures which are cross aligned to extract statistics describing the variation in the user's signatures. Both systems have similar verification methodology and differ only in data acquisition and feature extraction modules. A test signature's authenticity is established by first aligning it with each reference signature of the claimed user, resulting in a number of dissimilarity scores: distances to nearest, farthest and template reference signatures. In previous systems, only one of these distances, typically the distance to the nearest reference signature or the distance to a template signature, was chosen, in an ad-hoc manner, to classify the signature as genuine or forgery. Here we propose a method to utilize all of these distances, treating them as features in a two-class classification problem, using standard pattern classification techniques. The distances are first normalized, resulting in a three dimensional space where genuine and forgery signature distributions are well separated. We experimented with the Bayes classifier, Support Vector Machines, and a linear classifier used in conjunction with Principal Component Analysis, to classify a given signature into one of the two classes (forgery or genuine). Test data sets of 620 on-line and 100 off-line signatures were constructed to evaluate performances of the two systems. Since it is very difficult to obtain real forgeries, we obtained skilled forgeries which are supplied by forgers who had access to signature data to practice before forging. The online system has a 1.4% error in rejecting forgeries, while rejecting only 1.3% of genuine signatures. As an offine signature is easier to forge, the offine system's performance is lower: a 25% error in rejecting forgery signatures and 20% error in rejecting genuine signatures. The results for the online system show significant improvement over the state-of-the-art results, and the results for the offline system are comparable with the performance of experienced human examiners

Journal of Telecommunications and Information Technology, 2010, nr 4

kwartalni

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book

Privacy-aware Security Applications in the Era of Internet of Things

In this dissertation, we introduce several novel privacy-aware security applications. We split these contributions into three main categories: First, to strengthen the current authentication mechanisms, we designed two novel privacy-aware alternative complementary authentication mechanisms, Continuous Authentication (CA) and Multi-factor Authentication (MFA). Our first system is Wearable-assisted Continuous Authentication (WACA), where we used the sensor data collected from a wrist-worn device to authenticate users continuously. Then, we improved WACA by integrating a noise-tolerant template matching technique called NTT-Sec to make it privacy-aware as the collected data can be sensitive. We also designed a novel, lightweight, Privacy-aware Continuous Authentication (PACA) protocol. PACA is easily applicable to other biometric authentication mechanisms when feature vectors are represented as fixed-length real-valued vectors. In addition to CA, we also introduced a privacy-aware multi-factor authentication method, called PINTA. In PINTA, we used fuzzy hashing and homomorphic encryption mechanisms to protect the users\u27 sensitive profiles while providing privacy-preserving authentication. For the second privacy-aware contribution, we designed a multi-stage privacy attack to smart home users using the wireless network traffic generated during the communication of the devices. The attack works even on the encrypted data as it is only using the metadata of the network traffic. Moreover, we also designed a novel solution based on the generation of spoofed traffic. Finally, we introduced two privacy-aware secure data exchange mechanisms, which allow sharing the data between multiple parties (e.g., companies, hospitals) while preserving the privacy of the individual in the dataset. These mechanisms were realized with the combination of Secure Multiparty Computation (SMC) and Differential Privacy (DP) techniques. In addition, we designed a policy language, called Curie Policy Language (CPL), to handle the conflicting relationships among parties. The novel methods, attacks, and countermeasures in this dissertation were verified with theoretical analysis and extensive experiments with real devices and users. We believe that the research in this dissertation has far-reaching implications on privacy-aware alternative complementary authentication methods, smart home user privacy research, as well as the privacy-aware and secure data exchange methods

Enhanced Face Liveness Detection Based on Features From Nonlinear Diffusion Using Specialized Deep Convolution Network And Its Application In OAuth

The major contribution of this research is the development of enhanced algorithms that will prevent face spoofing attacks by utilizing a single image captured from a 2-D printed image or a recorded video. We first apply a nonlinear diffusion based on an additive operator splitting (AOS) scheme with a large time step to acquire a diffused image. The AOS-based scheme enables fast diffusion that successfully reveals the depth information and surface texture in the input image. Then a specialized deep convolution neural network is developed that can extract the discriminative and high-level features of the input diffused image to differentiate between a fake face and a real face. Our proposed method yields higher accuracy as compared to the previously implemented state-of-the-art methods. As an application of the face liveness detection, we develop face biometric authentication in an Open Authorization (OAuth) framework for controlling secure access to web resources. We implement a complete face verification system that consists of face liveness detection followed by face authentication that uses Local Binary Pattern as features for face recognition. The entire face authentication process consists of four services: an image registration service, a face liveness detection service, a verification service, and an access token service for use in OAuth