A framework for forensic face recognition based on recognition performance calibrated for the quality of image pairs

Recently, it has been shown that performance of a face recognition system depends on the quality of both face images participating in the recognition process: the reference and the test image. In the context of forensic face recognition, this observation has two implications: a) the quality of the trace (extracted from CCTV footage) constrains the performance achievable using a particular face recognition system; b) the quality of the suspect reference set (to which the trace is matched against) can be judiciously chosen to approach optimal recognition performance under such a constraint. Motivated by these recent findings, we propose a framework for forensic face recognition that is based on calibrating the recognition performance for the quality of pairs of images. The application of this framework to several mock-up forensic cases, created entirely from the MultiPIE dataset, shows that optimal recognition performance, under such a constraint, can be achieved by matching the quality (pose, illumination, and, imaging device) of the reference set to that of the trace. This improvement in recognition performance helps reduce the rate of misleading interpretation of the evidence

Forensic speaker recognition

The aim of forensic speaker recognition is to establish links between individuals and criminal activities, through audio speech recordings. This field is multidisciplinary, combining predominantly phonetics, linguistics, speech signal processing, and forensic statistics. On these bases, expert-based and automatic approaches have been developed to analyze the speaker's utterances on recordings, usually originating from anonymous calls, wiretapping procedures, and covert audio surveillance. Most of the forensic laboratories still opt for either of these two approaches, even though, in many respects, they appear to be complementary. The main requirements for these methods are independence to the text, ability to handle minimal length recordings, and a superior robustness regarding noise, transmission channels, and other variations of the recording conditions. Forensic speaker recognition can be considered a forerunner in the implementation of a logical inference framework to estimate the value of the evidence from the analytical results. The limits of forensic speaker recognition are the absence of a fixed and known number of highly discriminatory features in speech, the limited quality of the audio recordings captured in forensic conditions, and the application of recognition approaches in the absence of any known underlying model that accurately represents the speaker-dependent information

Friction ridge skin - Automated Fingerprint Identification System (AFIS)

This contribution describes the development and the forensic use of automated fingerprint identification systems (AFISs). AFISs were initially developed in order to overcome the limitations of the paper-based fingerprint collections, by digitizing the ten-print cards in computerized databases and to translate the manual pattern classification into computer-friendly codes. Then, technologies to automate the fingerprint feature extraction and comparison were developed, and AFISs were implemented on a large scale in order to improve the process of identification of repetitive offenders based on the ten-print cards. Further development of the fingerprint biometric technology allowed for the inclusion of palmprint reference databases and for the processing of fingermarks and palmmarks with, as a result, the partial automation of the forensic investigation and intelligence process. In the field of AFIS, the challenges for the future call for further automation of the feature extraction from low-quality fingerprint and fingermark images, for more transparency in the processes, for the improvement of the interoperability of the systems on a global level and the combination of biometric modalities as well as for the use of fingerprint biometric technology and scientific methodology, to further develop the forensic friction ridge evaluation process

Biometrics — Developments and Potential

This article describes the use of biometric technology in forensic science, for the development of new methods and tools, improving the current forensic biometric applications, and allowing for the creation of new ones. The article begins with a definition and a summary of the development of this field. It then describes the data and automated biometric modalities of interest in forensic science and the forensic applications embedding biometric technology. On this basis, it describes the solutions and limitations of the current practice regarding the data, the technology, and the inference models. Finally, it proposes research orientations for the improvement of the current forensic biometric applications and suggests some ideas for the development of some new forensic biometric applications

Validation of likelihood ratio methods for forensic evidence evaluation handling multimodal score distributions

This paper is a postprint of a paper submitted to and accepted for publication in IET Biometrics and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital LibraryThis article presents a method for computing Likelihood Ratios (LR) from multimodal score distributions produced by an Automated Fingerprint Identification System (AFIS) feature extraction and comparison algorithm. The AFIS algorithm used to compare fingermarks and fingerprints was primarily developed for forensic investigation rather than for forensic evaluation. The computation of the scores is speed-optimized and performed on three different stages, each of which outputs discriminating scores of different magnitudes together forming a multimodal score distribution. It is worthy mentioning that each fingermark to fingerprint comparison performed by the AFIS algorithm results in one single similarity score (e.g. one score per comparison). The multimodal nature of the similarity scores can be typical for other biometric systems and the method proposed in this work can be applied in similar cases, where the multimodal nature in similarity scores is observed. In this work we address some of the problems related to modelling such distributions and propose solutions to issues like data sparsity, dataset shift and over-fitting. The issues mentioned affect the methods traditionally used in the situation when a multimodal nature in the similarity scores is observed (a Kernel Density Functions (KDF) was used to illustrate these issues in our case). Furthermore, the method proposed produces interpretable results in the situations when the similarity scores are sparse and traditional approaches lead to erroneous LRs of huge magnitudesThe research was conducted in scope of the BBfor2 – Marie Curie Initial Training Network (FP7-PEOPLE-ITN-2008 under the Grant Agreement 238803) at the Netherlands Forensic Institute in cooperation with the ATVS Biometric Recognition Group at the Universidad Autonoma de Madrid and the National Police Services Agency of the Netherland

Likelihood ratio data to report the validation of a forensic fingerprint evaluation method

Data to which the authors refer to throughout this article are likelihood ratios (LR) computed from the comparison of 5–12 minutiae fingermarks with fingerprints. These LRs data are used for the validation of a likelihood ratio (LR) method in forensic evidence evaluation. These data present a necessary asset for conducting validation experiments when validating LR methods used in forensic evidence evaluation and set up validation reports. These data can be also used as a baseline for comparing the fingermark evidence in the same minutiae configuration as presented in (D. Meuwly, D. Ramos, R. Haraksim,) [1], although the reader should keep in mind that different feature extraction algorithms and different AFIS systems used may produce different LRs values. Moreover, these data may serve as a reproducibility exercise, in order to train the generation of validation reports of forensic methods, according to [1]. Alongside the data, a justification and motivation for the use of methods is given. These methods calculate LRs from the fingerprint/mark data and are subject to a validation procedure. The choice of using real forensic fingerprint in the validation and simulated data in the development is described and justified. Validation criteria are set for the purpose of validation of the LR methods, which are used to calculate the LR values from the data and the validation report. For privacy and data protection reasons, the original fingerprint/mark images cannot be shared. But these images do not constitute the core data for the validation, contrarily to the LRs that are shared.The research was conducted in scope of the BBfor2 – European Commission Marie Curie Initial Training Network (FP7-PEOPLE-ITN 2008 under Grant Agreement 238803) in cooperation with The Netherlands Forensic Institute and the ATVS Biometric Recognition Group at the Universidad Autonoma de Madrid

Mind the Gap:A practical framework for classifiers in a forensic context

In this paper, we present a practical framework that addresses six, mostly forensic, aspects that can be considered during the design and evaluation of biometric classifiers for the purpose of forensic evidence evaluation. Forensic evidence evaluation is a central activity in forensic case work, it includes the assessment of strength of evidence of trace and reference specimens and its outcome may be used in a court of law. The addressed aspects consider the modality and features, the biometric score and its forensic use, and choice and evaluation of several performance characteristics and metrics. The aim of the framework is to make the design and evaluation choices more transparent. We also present two applications of the framework pertaining to forensic face recognition. Using the framework, we can demonstrate large and explainable variations in discriminating power between subjects

From biometric scores to forensic likelihood ratios

In this chapter, we describe the issue of the interpretation of forensic evidence from scores computed by a biometric system. This is one of themost important topics into the so-called area of forensic biometrics.We will show the importance of the topic, introducing some of the key concepts of forensic science with respect to the interpretation of results prior to their presentation in court, which is increasingly addressed by the computation of likelihood ratios (LR). We will describe the LR methodology, and will illustrate it with an example of the evaluation of fingerprint evidence in forensic conditions, by means of a fingerprint biometric system.</p