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

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

Multibiometric security in wireless communication systems

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 05/08/2010.This thesis has aimed to explore an application of Multibiometrics to secured wireless communications. The medium of study for this purpose included Wi-Fi, 3G, and WiMAX, over which simulations and experimental studies were carried out to assess the performance. In specific, restriction of access to authorized users only is provided by a technique referred to hereafter as multibiometric cryptosystem. In brief, the system is built upon a complete challenge/response methodology in order to obtain a high level of security on the basis of user identification by fingerprint and further confirmation by verification of the user through text-dependent speaker recognition. First is the enrolment phase by which the database of watermarked fingerprints with memorable texts along with the voice features, based on the same texts, is created by sending them to the server through wireless channel. Later is the verification stage at which claimed users, ones who claim are genuine, are verified against the database, and it consists of five steps. Initially faced by the identification level, one is asked to first present one’s fingerprint and a memorable word, former is watermarked into latter, in order for system to authenticate the fingerprint and verify the validity of it by retrieving the challenge for accepted user. The following three steps then involve speaker recognition including the user responding to the challenge by text-dependent voice, server authenticating the response, and finally server accepting/rejecting the user. In order to implement fingerprint watermarking, i.e. incorporating the memorable word as a watermark message into the fingerprint image, an algorithm of five steps has been developed. The first three novel steps having to do with the fingerprint image enhancement (CLAHE with 'Clip Limit', standard deviation analysis and sliding neighborhood) have been followed with further two steps for embedding, and extracting the watermark into the enhanced fingerprint image utilising Discrete Wavelet Transform (DWT). In the speaker recognition stage, the limitations of this technique in wireless communication have been addressed by sending voice feature (cepstral coefficients) instead of raw sample. This scheme is to reap the advantages of reducing the transmission time and dependency of the data on communication channel, together with no loss of packet. Finally, the obtained results have verified the claims

Recognizing Human Faces: Physical Modeling and Pattern Classification

Although significant work has been done in the field of face recognition, the performance of the state-of-the-art face recognition algorithms is not good enough to be effective in operational systems. Most algorithms work well for controlled images but are quite susceptible to changes in illumination, pose, etc. In this dissertation, we propose methods which address these issues, to recognize faces in more realistic scenarios. The developed approaches show the importance of physical modeling, contextual constraints and pattern classification for this task. For still image-based face recognition, we develop an algorithm to recognize faces illuminated by arbitrarily placed, multiple light sources, given just a single image. Though the problem is ill-posed in its generality, linear approximations to the subspace of Lambertian images in combination with rank constraints on unknown facial shape and albedo are used to make it tractable. In addition, we develop a purely geometric illumination-invariant matching algorithm that makes use of the bilateral symmetry of human faces. In particular, we prove that the set of images of bilaterally symmetric objects can be partitioned into equivalence classes such that it is always possible to distinguish between two objects belonging to different equivalence classes using just one image per object. For recognizing faces in videos, the challenge lies in suitable characterization of faces using the information available in the video. We propose a method that models a face as a linear dynamical system whose appearance changes with pose. Though the proposed method performs very well on the available datasets, it does not explicitly take the 3D structure or illumination conditions into account. To address these issues, we propose an algorithm to perform 3D facial pose tracking in videos. The approach combines the structural advantages of geometric modeling with the statistical advantages of a particle filter based inference to recover the 3D configuration of facial features in each frame of the video. The recovered 3D configuration parameters are further used to recognize faces in videos. From a pattern classification point of view, automatic face recognition presents a very unique challenge due to the presence of just one (or a few) sample(s) per identity. To address this, we develop a cohort-based framework that makes use of the large number of non-match samples present in the database to improve verification and identification performance

Securing Cloud Storage by Transparent Biometric Cryptography

With the capability of storing huge volumes of data over the Internet, cloud storage has become a popular and desirable service for individuals and enterprises. The security issues, nevertheless, have been the intense debate within the cloud community. Significant attacks can be taken place, the most common being guessing the (poor) passwords. Given weaknesses with verification credentials, malicious attacks have happened across a variety of well-known storage services (i.e. Dropbox and Google Drive) – resulting in loss the privacy and confidentiality of files. Whilst today's use of third-party cryptographic applications can independently encrypt data, it arguably places a significant burden upon the user in terms of manually ciphering/deciphering each file and administering numerous keys in addition to the login password. The field of biometric cryptography applies biometric modalities within cryptography to produce robust bio-crypto keys without having to remember them. There are, nonetheless, still specific flaws associated with the security of the established bio-crypto key and its usability. Users currently should present their biometric modalities intrusively each time a file needs to be encrypted/decrypted – thus leading to cumbersomeness and inconvenience while throughout usage. Transparent biometrics seeks to eliminate the explicit interaction for verification and thereby remove the user inconvenience. However, the application of transparent biometric within bio-cryptography can increase the variability of the biometric sample leading to further challenges on reproducing the bio-crypto key. An innovative bio-cryptographic approach is developed to non-intrusively encrypt/decrypt data by a bio-crypto key established from transparent biometrics on the fly without storing it somewhere using a backpropagation neural network. This approach seeks to handle the shortcomings of the password login, and concurrently removes the usability issues of the third-party cryptographic applications – thus enabling a more secure and usable user-oriented level of encryption to reinforce the security controls within cloud-based storage. The challenge represents the ability of the innovative bio-cryptographic approach to generate a reproducible bio-crypto key by selective transparent biometric modalities including fingerprint, face and keystrokes which are inherently noisier than their traditional counterparts. Accordingly, sets of experiments using functional and practical datasets reflecting a transparent and unconstrained sample collection are conducted to determine the reliability of creating a non-intrusive and repeatable bio-crypto key of a 256-bit length. With numerous samples being acquired in a non-intrusive fashion, the system would be spontaneously able to capture 6 samples within minute window of time. There is a possibility then to trade-off the false rejection against the false acceptance to tackle the high error, as long as the correct key can be generated via at least one successful sample. As such, the experiments demonstrate that a correct key can be generated to the genuine user once a minute and the average FAR was 0.9%, 0.06%, and 0.06% for fingerprint, face, and keystrokes respectively. For further reinforcing the effectiveness of the key generation approach, other sets of experiments are also implemented to determine what impact the multibiometric approach would have upon the performance at the feature phase versus the matching phase. Holistically, the multibiometric key generation approach demonstrates the superiority in generating the bio-crypto key of a 256-bit in comparison with the single biometric approach. In particular, the feature-level fusion outperforms the matching-level fusion at producing the valid correct key with limited illegitimacy attempts in compromising it – 0.02% FAR rate overall. Accordingly, the thesis proposes an innovative bio-cryptosystem architecture by which cloud-independent encryption is provided to protect the users' personal data in a more reliable and usable fashion using non-intrusive multimodal biometrics.Higher Committee of Education Development in Iraq (HCED

Evaluating the Performance of a Large-Scale Facial Image Dataset Using Agglomerated Match Score Statistics

Biometrics systems are experiencing wide-spread usage in identification and access control applications. To estimate the performance of any biometric systems, their characteristics need to be analyzed to make concrete conclusions for real time usage. Performance testing of hardware or software components of either custom or state-of-the-art commercial biometric systems is typically carried out on large datasets. Several public and private datasets are used in current biometric research. West Virginia University has completed several large scale multimodal biometric data collection with an aim to create research datasets that can be used by disciplines concerning secured biometric applications. However, the demographic and image quality properties of these datasets can potentially lead to bias when they are used in performance testing of new systems. To overcome this, the characteristics of datasets used for performance testing must be well understood prior to usage.;This thesis will answer three main questions associated with this issue:;• For a single matcher, do the genuine and impostor match score distributions within specific demographics groups vary from those of the entire dataset? • What are the possible ways to compare the subset of demographic match score distributions against those of the entire dataset? • Based on these comparisons, what conclusions can be made about the characteristics of dataset?;In this work, 13,976 frontal face images from WVU\u27s 2012 Biometric collection project funded by the FBI involving 1200 individuals were used as a \u27test\u27 dataset. The goal was to evaluate performance of this dataset by generating genuine and impostor match scores distributions using a commercial matching software Further, the dataset was categorized demographically, and match score distributions were generated for these subsets in order to explore whether or not this breakdown impacted match score distributions. The match score distributions of the overall dataset were compared against each demographic cohorts.;Using statistical measures, Area under Curve (AUC) and Equal Error Rate (EER) were observed by plotting Receiver Operating Characteristics (ROC) curves to measure the performance of each demographic group with respect to overall data and also within the cohorts of demographic group. Also, Kull-back Leibler Divergence and Jensen Shannon Divergence values were calculated for each demographic cohort (age, gender and ethnicity) within the overall data. These statistical approaches provide a numerical value representing the amount of variation between two match score distributions In addition, FAR and FRR was observed to estimate the error rates. These statistical measures effectively enabled the determination of the impact of different demographic breakdown on match score distributions, and thus, helped in understanding the characteristics of dataset and how they may impact its usage in performance testing biometrics

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