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A new multimodal biometric database, acquired in the framework of the BiosecurID project, is presented together with the description of the acquisition setup and protocol. The database includes eight unimodal biometric traits, namely: speech, iris, face (still images, videos of talking faces), handwritten signature and handwritten text (on-line dynamic signals, off-line scanned images), fingerprints (acquired with two different sensors), hand (palmprint, contour-geometry) and keystroking. The database comprises 400 subjects and presents features such as: realistic acquisition scenario, balanced gender and population distributions, availability of information about particular demographic groups (age, gender, handedness), acquisition of replay attacks for speech and keystroking, skilled forgeries for signatures, and compatibility with other existing databases. All these characteristics make it very useful in research and development of unimodal and multimodal biometric systems.

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Calculation of likelihood ratios (LR) in evidence evaluation still presents major challenges in many forensic disciplines: for instance, an incorrect selection of databases, a bad choice of statistical models, low quantity and bad quality of the evidence are factors that may lead to likelihood ratios supporting the wrong proposition in a given case. However, measuring performance of LR values is not straightforward, and adequate metrics should be defined and used. With this objective, in this work we describe the concept of calibration, a property of a set of LR values. We highlight that some desirable behavior of LR values happens if they are well calibrated. Moreover, we propose a tool for representing performance, the Empirical Cross-Entropy (ECE) plot, showing that it can explicitly measure calibration of LR values. We finally describe some examples using speech evidence, where the usefulness of ECE plots and the measurement of calibration is shown.

Esta es la versión de autor del artículo publicado en: This is an author produced version of a paper published in: In this article we propose a framework for the scientific assessment of the performance of forensic evidence evaluation methods which express the value of the evidence in the form of a LR. This framework is based on Information Theory (10), a field which allows an intuitive interpretation of the results of the scientific assessment, which is a highly desirable characteristic when results have to be reported to a court of law. The proposed framework, based on a performance metric called Empirical Cross-Entropy (ECE), can be used with any LR-based evidence evaluation method at any level of the propositions stated in the case (source, activity or offence) (11). The main contribution 5 ! of this work is the full description of the assessment framework in a forensic context, and its generalisation to any forensic discipline.A convenient graphical representation of the performance is also proposed, namely ECE plots. Although ECE plots have been introduced in (12) in the context of forensic speaker recognition, this article presents novel contributions with respect to such work, with a significant extension at the methodological, application and experimental level.First, we generalise the use of ECE to any forensic field, not only forensic automatic speaker recognition. This represents a fundamental advance in the applicability of the proposed framework, since each different forensic field presents particular types of data and models. In particular, automatic speaker recognition yields continuous, univariate data. On the other hand, glass analysis, as presented here, generates multivariate data.Secondly, this article contributes a set of recommendations for practitioners, which simulate typical scenarios for the use of the proposed assessment tools in daily forensic casework. To this end, the authors have developed publicly available free software implementing ECE plots as proposed in this article (available at http://arantxa.ii.uam.es/~dramos/software.html). The use of this methodology is exemplified by a case example using glass profiles obtained from real forensic cases, where several evidence evaluation methods are compared prior to their application to the case, in order to illustrate the recommendations given.A remark is in order here. Although we give recommendations for the use of ECE in court, we realize that meaningful interpretation of its value in a legal process seems currently unrealistic until a deeper understanding of the Bayesian approach has been established across all the actors participating in a forensic case. Nevertheless, as it has

El acceso a la versión del editor puede requerir la suscripción del recurso Access to the published version may require subscription AbstractThis paper reports an exhaustive analysis of the discriminative power of the different regions of the human face on various forensic scenarios. In practice, when forensic examiners compare two face images, they focus their attention not only on the overall similarity of the two faces. They carry out an exhaustive morphological comparison region by region (e.g., nose, mouth, eyebrows, etc.). In this scenario it is very important to know based on scientific methods to what extent each facial region can help in identifying a person. This knowledge obtained using quantitative and statical methods on given populations can then be used by the examiner to support or tune his observations. In order to generate such scientific knowledge useful for the expert, several methodologies are compared, such as manual and automatic facial landmarks extraction, different facial regions extractors, and various distances between the subject and the acquisition camera. Also, three scenarios of interest for forensics are considered comparing mugshot and Closed-Circuit TeleVision (CCTV) face images using MORPH and SCface databases. One of the findings is that depending of the acquisition distances, the discriminative power of the facial regions change, having in some cases better performance than the full face.

Abstract-As biometric technology is increasingly deployed, it will be common to replace parts of operational systems with newer designs. The cost and inconvenience of reacquiring enrolled users when a new vendor solution is incorporated makes this approach difficult and many applications will require to deal with information from different sources regularly. These interoperability problems can dramatically affect the performance of biometric systems and thus, they need to be overcome. Here, we describe and evaluate the ATVS-UAM fusion approach submitted to the quality-based evaluation of the 2007 BioSecure Multimodal Evaluation Campaign, whose aim was to compare fusion algorithms when biometric signals were generated using several biometric devices in mismatched conditions. Quality measures from the raw biometric data are available to allow system adjustment to changing quality conditions due to device changes. This system adjustment is referred to as quality-based conditional processing. The proposed fusion approach is based on linear logistic regression, in which fused scores tend to be log-likelihood-ratios. This allows the easy and efficient combination of matching scores from different devices assuming low dependence among modalities. In our system, quality information is used to switch between different system modules depending on the data source (the sensor in our case) and to reject channels with low quality data during the fusion. We compare our fusion approach to a set of rule-based fusion schemes over normalized scores. Results show that the proposed approach outperforms all the rule-based fusion schemes. We also show that with the quality-based channel rejection scheme, an overall improvement of 25% in the equal error rate is obtained.