In this study, the authors present a hierarchical algorithm to register a partial fingerprint against a full fingerprint using only the orientation fields. In the first level, they shortlist possible locations for registering the partial fingerprint in the full fingerprint using a normalised correlation measure, taking various rotations into account. As a second level, on those candidate locations, they calculate three other similarity measures. They then perform score fusion for all the estimated similarity scores to locate the final registration. By registering a partial fingerprint against a full fingerprint, they can reduce the search space of the minutiae set in the full fingerprint, thereby improving the result of partial fingerprint identification, particularly for poor quality latent fingerprints. They report the rank identification improvements of two minutiae-based automated fingerprint identification systems on the National Institute of Standards and Technology (NIST)-Special Database 27 database when they use the authors hierarchical registration as a pre-alignment.

A high resolution palmprint recognition system using full or partial, eventually degraded, palmprints is presented. Previous work on palmprint matching addressed mostly commercial applications, using low resolution images. However, in forensic scenarios, high resolution palmprints, although incomplete and/or degraded, are often used. Degradations may result from surface irregularities or impurities, which are often modelled as Gaussian or salt and pepper noise, as well as smearing of the palmprint because of hand sliding, which in this work is modelled as motion blur. The proposed system matches palmprints, full or partial, undegraded or subjected to one of the above degradations, against palmprints registered in a database. The proposed system extends previous work of the authors by adaptively selecting between two palmprint matching approaches, achieving better recognition results than either of the two individual strategies. The first approach relies on a motion blur compensation technique, while the second is based on a combination of the Fourier–Mellin transform with a modified phase-only correlation matching strategy. The presented results show that for sufficiently large palmprint areas the blur compensation technique works better, while for small-sized partial palmprints with large motion blur degradation values the second approach based on correlation is preferred.

Gait is a promising modality for forensic science because it has discrimination ability even if the gait features are extracted from low-quality image sequences captured at a distance. However, in forensic cases the observation view is often different, leading to accuracy degradation. Therefore the authors propose a gait recognition algorithm that achieves high accuracy in cases where observation views are different. They used a view transformation technique, and generated multiple joint gait features by changing the source gait features. They formed a hypothesis that the multiple transformed features and original features should be similar to each other if the target subjects are the same. They calculated multiple scores that measured the consistency of the features, and a likelihood ratio from the scores. To evaluate the accuracy of the proposed method, they drew Tippett plots and empirical cross-entropy plots, together with cumulative match characteristic curves and receiver operator characteristic curves, and evaluated discrimination ability and calibration quality. The results showed that their proposed method achieves good results in terms of discrimination and calibration.

Periocular recognition has gained attention recently due to demands of increased robustness of face or iris in less controlled scenarios. We present a new system for eye detection based on complex symmetry filters, which has the advantage of not needing training. Also, separability of the filters allows faster detection via one-dimensional convolutions. This system is used as input to a periocular algorithm based on retinotopic sampling grids and Gabor spectrum decomposition. The evaluation framework is composed of six databases acquired both with near-infrared and visible sensors. The experimental setup is complemented with four iris matchers, used for fusion experiments. The eye detection system presented shows very high accuracy with near-infrared data, and a reasonable good accuracy with one visible database. Regarding the periocular system, it exhibits great robustness to small errors in locating the eye centre, as well as to scale changes of the input image. The density of the sampling grid can also be reduced without sacrificing accuracy. Lastly, despite the poorer performance of the iris matchers with visible data, fusion with the periocular system can provide an improvement of more than 20%. The six databases used have been manually annotated, with the annotation made publicly available.

Handwriting biometrics has a long history, especially when the handwritten signature is the target, but it has also proved possible to use handwriting as a basis for the prediction of various non-unique but forensically useful characteristics of the writer, generally considered to be examples of so-called ‘soft biometrics’. Most commonly, these are characteristics such as the age or gender of the writer, but the predictive capabilities arising in handwriting offer wider opportunities for trait prediction. This study presents a preliminary investigation of the use of handwriting to predict information about the writer relating specifically to higher level characteristics such as emotional state. The authors present an initial study to demonstrate that this is possible, and explore a number of factors particularly relevant to the use of such a capability in areas of forensic investigation.

This work presents an approach for combining texture and shape feature sets towards age-invariant face recognition. Physiological studies have proven that the human visual system can recognise familiar faces at different ages from the face outline alone. Based on this scientific fact, the phase congruency features for shape analysis were adopted to produce a face edge map. This was beneficial in tracking the craniofacial growth pattern for each subject. Craniofacial growth is common during childhood years, but after the age of 18, the texture variations start to show as the effect of facial aging. Therefore, in order to handle such texture variations, a variance of the well-known local binary pattern (LBP) texture descriptor, known as LBP variance was adopted. The results showed that fusing the shape and the texture features set yielded better performance than the individual performance of each feature set. Moreover, the individual verification accuracy for each feature set was improved when they were transformed to a kernel discriminative common vectors presentation. The system achieved an overall verification accuracy of above 93% when it was evaluated over the FG-NET face aging database.

(Voice-) biometric data is considered as personally identifiable information, that is, the increasing demand on (mobile) speaker recognition systems calls for applications which prevent from privacy threats, such as identity-theft or tracking without consent. Technologies of biometric template protection, in particular biometric cryptosystems, fulfil standardised properties of irreversibility and unlinkability which represent appropriate countermeasures to such vulnerabilities of conventional biometric recognition systems. Thereby, public confidence in and social acceptance of biometric applications is strengthened. In this work the authors propose a binarisation technique, which is used to extract scalable high-entropy binary voice reference data (templates) from speaker models, based on Gaussian mixture models and universal background models. Binary feature vectors are then protected within a template protection scheme in particular, fuzzy commitment scheme, in which error correction list-decoding is employed to overcome high intra-class variance of voice samples. In experiments, which are evaluated out on a text-independent speaker corpus of 339 individuals, it is demonstrated that the fully ISO/IEC IS 24745 compliant system achieves privacy protection at a negligible loss of biometric performance, confirming the soundness of the presented approach.

This study describes a special-case application of speaker recognition in open-set speaker-identification mode, which nonetheless has wide applicability. Watch-list based speaker spotting in telephone banking can potentially provide valuable protection against ‘known’ fraudsters with access to stolen customer details. In this study, the detection of known fraudsters in a telephone banking service using commercial off-the-shelf verification engines is described. A new ‘delta scoring’ method for watch-list detection is proposed based on using the genuine customer model as a reference. The approach combines for the first time speaker recognition in both verification and identification mode. Empirical experiment results show a significant gain in performance using the new method.