This study discusses a 10-year effort by Standards Committee 37 of the International Organisation for Standardisation/International Electrotechnical Commission Joint Technical Committee 1 (ISO/IEC JTC1 SC37) to create a systematic vocabulary for the field of ‘biometrics’ based on international standards for vocabulary development. That process has now produced a new International Standard (ISO/IEC 2382-37:2012), which conceptualises and defines 121 terms that are most central to the proposed field. This study will review some of the philosophical and operational principles of vocabulary development within SC37, present 11 of the most commonly used standardised terms with their definitions and discuss some of the conceptual changes implicit in the new vocabulary.

This study introduces a linear Gaussian model-based framework for voice biometrics. The model works with discrete-time linear dynamical systems. The study motivation is to use the linear Gaussian modelling method in voice biometrics, and show that the accuracy offered by the linear Gaussian modelling method is comparable with other state-of-the-art methods such as Probabilistic Linear Discriminant Analysis and two-covariance model. An expectation–maximisation algorithm is derived to train the model and a Bayesian solution is used to calculate the log-likelihood ratio score of all trials of speakers. This approach performed well on the core-extended conditions of the NIST 2010 Speaker Recognition Evaluation, and is competitive compared with the Gaussian probabilistic linear discriminant analysis, in terms of normalised decision cost function.

A practically viable multi-biometric recognition system should not only be stable, robust and accurate but should also adhere to real-time processing speed and memory constraints. This study proposes a cascaded classifier-based framework for use in biometric recognition systems. The proposed framework utilises a set of weak classifiers to reduce the enrolled users’ dataset to a small list of candidate users. This list is then used by a strong classifier set as the final stage of the cascade to formulate the decision. At each stage, the candidate list is generated by a Mahalanobis distance-based match score quality measure. One of the key features of the authors framework is that each classifier in the ensemble can be designed to use a different modality thus providing the advantages of a truly multimodal biometric recognition system. In addition, it is one of the first truly multimodal cascaded classifier-based approaches for biometric recognition. The performance of the proposed system is evaluated both for single and multimodalities to demonstrate the effectiveness of the approach.

The concept of cancellable biometrics has been introduced as a way to overcome privacy concerns surrounding the management of biometric data. The goal is to transform a biometric trait into a new but revocable representation for enrolment and identification/verification. Thus, if compromised, a new representation of original biometric data can be generated. In addition, multi-biometric systems are increasingly being deployed in various biometric-based applications because of their advantages over uni-biometric systems. In this study, the authors specifically investigate the use of ensemble systems and cancellable transformations for the multi-biometric context, and the authors use as examples two different biometric modalities (fingerprint and handwritten signature) separately and in the multi-modal context (multi-biometric). The datasets to be used in this analysis were FVC2004 (fingerprint verification competition) for fingerprint and an in-house database for signature. To increase the effectiveness of the proposed ensemble systems, two feature selection (FS) methods will be used to distribute the attributes among the individual classifiers of an ensemble, increasing diversity and performance of such systems. As a result of this analysis, they will observe that the use of a cancellable transformation in the multi-biometric dataset increased accuracy level for the ensemble systems, mainly when using FS methods.