Andrew Beng Jin Teoh

Random Multispace Quantization as an Analytic Mechanism for BioHashing of Biometric and Random Identity Inputs

Biometric analysis for identity verification is becoming a widespread reality. Such implementations necessitate large-scale capture and storage of biometric data, which raises serious issues in terms of data privacy and (if such data is compromised) identity theft. These problems stem from the essential permanence of biometric data, which (unlike secret passwords or physical tokens) cannot be refreshed or reissued if compromised. Our previously presented biometric-hash framework prescribes the integration of external (password or token-derived) randomness with user-specific biometrics, resulting in bitstring outputs with security characteristics (i.e., noninvertibility) comparable to cryptographic ciphers or hashes. The resultant BioHashes are hence cancellable, i.e., straightforwardly revoked and reissued (via refreshed password or reissued token) if compromised. BioHashing furthermore enhances recognition effectiveness, which is explained in this paper as arising from the random multispace quantization (RMQ) of biometric and external random inputs

Cancellable biometrics and annotations on BioHash

Lately, the once powerful one-factor authentication which is based solely on either password, token or biometric approach, appears to be insufficient in addressing the challenges of identity frauds. For example, the sole biometric approach suffers from the privacy invasion and non-revocable issues. Passwords and tokens are easily forgotten and lost. To address these issues, the notion of cancellable biometrics was introduced to denote biometric templates that can be cancelled and replaced with the inclusion of another independent authentication factor. BioHash is a form of cancellable biometrics which mixes a set of user-specific random vectors with biometric features. In verification setting, BioHash is able to deliver extremely low error rates as compared to the sole biometric approach when a genuine token is used. However, this raises the possibility of two identity theft scenarios: (i) stolen-biometrics, in which an impostor possesses intercepted biometric data of sufficient high quality to be considered genuine and (ii) stolen-token, in which an impostor has access to the genuine token and used by the impostor to claim as the genuine user. We found that the recognition rate for the latter case is poorer. In this paper, the quantised random projection ensemble based on the Johnson-Lindenstrauss Lemma is used to establish the mathematical foundation of BioHash. Based on this model, we elucidate the characteristics of BioHash in pattern recognition as well as security view points and propose new methods to rectify the stolen-token problem.

Review article: Touch-less palm print biometrics: Novel design and implementation

In this paper, we propose an innovative touch-less palm print recognition system. This project is motivated by the publics demand for non-invasive and hygienic biometric technology. For various reasons, users are concerned about touching the biometric scanners. Therefore, we propose to use a low-resolution web camera to capture the users hand at a distance for recognition. The users do not need to touch any device for their palm print to be acquired. A novel hand tracking and palm print region of interest (ROI) extraction technique are used to track and capture the users palm in real-time video stream. The discriminative palm print features are extracted based on a new method that applies local binary pattern (LBP) texture descriptor on the palm print directional gradient responses. Experiments show promising result using the proposed method. Performance can be further improved when a modified probabilistic neural network (PNN) is used for feature matching. Verification can be performed in less than one second in the proposed system.

Personalised cryptographic key generation based on FaceHashing

Among the various computer security techniques practice today, cryptography has been identified as one of the most important solutions in the integrated digital security system. Cryptographic techniques such as encryption can provide very long passwords that are not required to be remembered but are in turn protected by simple password, hence defecting their purpose. In this paper, we proposed a novel two-stage technique to generate personalized cryptographic keys from the face biometric, which offers the inextricably link to its owner. At the first stage, integral transform of biometric input is to discretise to produce a set of bit representation with a set of tokenised pseudo random number, coined as FaceHash. In the second stage, FaceHash is then securely reduced to a single cryptographic key via Shamir secret-sharing. Tokenised FaceHashing is rigorously protective of the face data, with security comparable to cryptographic hashing of token and knowledge key-factor. The key is constructed to resist cryptanalysis even against an adversary who captures the user device or the feature descriptor.

A Survey of Keystroke Dynamics Biometrics

Research on keystroke dynamics biometrics has been increasing, especially in the last decade. The main motivation behind this effort is due to the fact that keystroke dynamics biometrics is economical and can be easily integrated into the existing computer security systems with minimal alteration and user intervention. Numerous studies have been conducted in terms of data acquisition devices, feature representations, classification methods, experimental protocols, and evaluations. However, an up-to-date extensive survey and evaluation is not yet available. The objective of this paper is to provide an insightful survey and comparison on keystroke dynamics biometrics research performed throughout the last three decades, as well as offering suggestions and possible future research directions.

Biometric hash: high-confidence face recognition

In this paper, we describe a biometric hash algorithm for robust extraction of bits from face images. While a face-recognition system has high acceptability, its accuracy is low. The problem arises because of insufficient capability of representing features and variations in data. Thus, we use dimensionality reduction to improve the capability to represent features, error correction to improve robustness with respect to within-class variations, and random projection and orthogonalization to improve discrimination among classes. Specifically, we describe several dimensionality-reduction techniques with biometric hashing enhancement for various numbers of bits extracted. The theoretical results are evaluated on the FERET face database showing that the enhanced methods significantly outperform the corresponding raw methods when the number of extracted bits reaches 100. The improvements of the postprocessing stage for principal component analysis (PCA), Wavelet Transform with PCA, Fisher linear discriminant, Wavelet Transform, and Wavelet Transform with Fourier-Mellin Transform are 98.02%, 95.83%, 99.46%, 99.16%, and 100%, respectively. The proposed technique is quite general, and can be applied to other biometric templates. We anticipate that this algorithm will find applications in cryptographically secure biometric authentication schemes.

2^N discretisation of biophasor in cancellable biometrics

BioPhasor was introduced as a form of cancellable biometrics which integrates a set of user-specific random numbers (RN) with biometric features. This BioPhasor was shown to fulfil diversity, reusability and performance requirements in cancellable biometrics formulation. In this paper, we reformulate and enhance the BioPhasor in terms of verification performance and security, through a 2N stage discretisation process. The formulation is experimented under two scenarios (legitimate and stolen RN) using 2400 FERET face images. Apart from the experiments, desired properties such as one-way transformation and diversity are also examined.

Cancellable biometerics featuring with tokenised random number

In this paper we introduce a novel cancellable biometric realisation approach based on the iterated inner products between the tokenised pseudo-random number and the face feature to produce a set of user-specific compact binary code, coined as FaceHash. This approach enables straightforward revocation of FaceHash via token replacement and there is no deterministic way to reveal FaceHash without having both tokenised random number and face feature, thus offers strong protection against biometrics fabrication. In addition, FaceHashing has significant functional advantages over contemporary biometrics like zero error rate and clean separation of the genuine and imposter populations.

Secure Hashing of Dynamic Hand Signatures Using Wavelet-Fourier Compression with BioPhasor Mixing and

We introduce a novel method for secure computation of biometric hash on dynamic hand signatures using BioPhasor mixing and discretization. The use of BioPhasor as the mixing process provides a one-way transformation that precludes exact recovery of the biometric vector from compromised hashes and stolen tokens. In addition, our user-specific discretization acts both as an error correction step as well as a real-to-binary space converter. We also propose a new method of extracting compressed representation of dynamic hand signatures using discrete wavelet transform (DWT) and discrete fourier transform (DFT). Without the conventional use of dynamic time warping, the proposed method avoids storage of users hand signature template. This is an important consideration for protecting the privacy of the biometric owner. Our results show that the proposed method could produce stable and distinguishable bit strings with equal error rates (EERs) of and for random and skilled forgeries for stolen token (worst case) scenario, and for both forgeries in the genuine token (optimal) scenario.

Statistical Fusion Approach on Keystroke Dynamics

Keystroke dynamics refers to a userpsilas habitual typing characteristics. These typing characteristics are believed to be unique among large populations. In this paper, we present a novel keystroke dynamic recognition system by using a fusion method. Firstly,we record the dwell time and the flight time as the feature data. We then calculate their mean and standard deviation values and stored. The test feature data will be transformed into the scores via Gaussian probability density function. On the other hand, we also propose a new technique, known as Direction Similarity Measure (DSM) to measure the differential of sign among each coupled characters in a phrase. Lastly, a weighted sum rule is applied by fusing the Gaussian scores and the DSM to enhance the final result. The best result of equal error rate 6.36% is obtained by using our home-made dataset.