Arnout C. Ruifrok

Interoperator test for anatomical annotation of earprints.

ABSTRACT: As part of the Forensic Ear Identification (FearID) research project, which aims to obtain estimators for the strength of evidence of earmarks found on crime scenes, a large database of earprints (over 1200 donors) has been collected. Starting from a knowledge‐based approach where experts add anatomical annotations of minutiae and landmarks present in prints, comparison of pairs of prints is done using the method of Vector Template Matching (VTM). As the annotation process is subjective, a validation experiment was performed to study its stability. Comparing prints on the basis of VTM, it appears that there are interoperator effects, individual operators yielding significantly more consistent results when annotating prints than different operators. The operators being well trained and educated, the observed variation on both clicking frequency and choice of annotation points suggests that implementation of the above is not the best way to go about objectifying earprint comparison. Processes like the above are relevant for any forensic science dealing with identification (e.g., of glass, tool marks, fibers, faces, fingers, handwriting, speakers) where manual (nonautomated) processes play a role. In these cases, results may be operator dependent and the dependencies need to be studied.

Repeatability and reproducibility of earprint acquisition.

Abstract:  For all forensic disciplines dealing with identification—e.g., of glass, tool marks, fibers, faces, fingers, handwriting, speakers—in which manual (subjective, nonautomated) processes play a role, operator dependencies are relevant. With respect to earprint identification, in the period 2002–2005, the Forensic Ear Identification research project collected a database of 1229 donors, three prints per ear, and laid down a “best practice” for print acquisition. Repeatability and reproducibility aspects of the print acquisition are tested. The study suggests that different operators may acquire prints of differing quality, with equal error rates of the matching system ranging from 9% to 19%. Moreover, it turns out that “matching” earprints are more alike when taken in a consecutive row than when taken on separate occasions. This underlines the importance of (1) studying operator effects, (2) operator training, and (3) not gathering “matching” reference material at the same occasion.

Facial image comparison using a 3D laser scanning system

To reliably perform comparisons of facial images, it is important to position the head corresponding to the facial images available. Techniques using three or more landmark points on the face have been proposed for matching the face and camera positions to the available photographs. However, these methods can be cumbersome, and require the cooperation of the subject. 3D photographs, together with 3D modeling software, offer the possibility of flexible and reproducable positioning of the head of a person corresponding to the face and camera position of the facial images. We will present our experiences with a non-contact 3D laser-scanning system (Minolta VI-900), especially with respect to ease-of-use, reproducabilty, and performance for facial comparison applications.

ForenFace: a unique annotated forensic facial image dataset and toolset

Few facial image datasets are suitable for forensic research. In this study, the authors present ForenFace, a facial image and video dataset. It contains video sequences and extracted images of 97 subjects recorded with six different surveillance camera of various types. Moreover, it also contains high-resolution images and 3D scans. The novelty of this dataset lies in two aspects: (i) a subset of 435 images (87 subjects, five images per subject) has been manually annotated, yielding a very rich forensically relevant annotation of almost 19.000 facial parts, and (ii) making available a toolset to create, view, and extract the annotation. The authors present protocols and the result of a baseline experiment in which two commercial software packages and an annotated facial feature contained in this dataset are compared. The dataset, the annotation and tools are available under a usage license.

Manually annotated characteristic descriptors: Measurability and variability

In this paper we study the measurability and variability of manually annotated characteristic descriptors on a forensic relevant face dataset. Characteristic descriptors are facial features (landmarks, shapes, etc.) that can be used during forensic case work. With respect to measurability, we observe that a significant proportion cannot be determined in images representative of forensic case work. Landmarks, closed and open shapes, and other forensic facial features show mostly that the variability depends on the image quality. Up to 50% of all considered evidential values are either positively or negatively influenced by annotator variability. However, when considering images with the lowest quality, we found that more than 70% of the evidential value intervals in principle could yield the wrong conclusion.

Extracting forensic evidence from biometric devices

Over the past few years, both large multinationals and governments have begun to contribute to even larger projects on biometric devices. Terrorist attacks in America and in other countries have highlighted the need for better identification systems for people as well as improved systems for controlling access to buildings. Another reason for investment in Research and Development in Biometric Devices, is the massive growth in internet-based systems -- whether for e-commerce, e-government or internal processes within organizations. The interface between the system and the user is routinely abused, as people have to remember many complex passwords and handle tokens of various types. In this paper an overview is given of the information that is important to know before an examination of such is systems can be done in a forensic proper way. In forensic evidence with biometric devices the forensic examiner should consider the possibilities of tampering with the biometric systems or the possibilities of unauthorized access before drawing conclusions.

Critical review of the use and scientific basis of forensic gait analysis

Abstract This review summarizes the scientific basis of forensic gait analysis and evaluates its use in the Netherlands, United Kingdom and Denmark, following recent critique on the admission of gait evidence in Canada. A useful forensic feature is (1) measurable, (2) consistent within and (3) different between individuals. Reviewing the academic literature, this article found that (1) forensic gait features can be quantified or observed from surveillance video, but research into accuracy, validity and reliability of these methods is needed; (2) gait is variable within individuals under differing and constant circumstances, with speed having major influence; (3) the discriminative strength of gait features needs more research, although clearly variation exists between individuals. Nevertheless, forensic gait analysis has contributed to several criminal trials in Europe in the past 15 years. The admission of gait evidence differs between courts. The methods are mainly observer-based: multiple gait analysts (independently) assess gait features on video footage of a perpetrator and suspect. Using gait feature databases, likelihood ratios of the hypotheses that the observed individuals have the same or another identity can be calculated. Automated gait recognition algorithms calculate a difference measure between video clips, which is compared with a threshold value derived from a video gait recognition database to indicate likelihood. However, only partly automated algorithms have been used in practice. We argue that the scientific basis of forensic gait analysis is limited. However, gait feature databases enable its use in court for supportive evidence with relatively low evidential value. The recommendations made in this review are (1) to expand knowledge on inter- and intra-subject gait variabilities, discriminative strength and interdependency of gait features, method accuracies, gait feature databases and likelihood ratio estimations; (2) to compare automated and observer-based gait recognition methods; to design (3) an international standard method with known validity, reliability and proficiency tests for analysts; (4) an international standard gait feature data collection method resulting in database(s); (5) (inter)national guidelines for the admission of gait evidence in court; and (6) to decrease the risk for cognitive and contextual bias in forensic gait analysis. This is expected to improve admission of gait evidence in court and judgment of its evidential value. Several ongoing research projects focus on parts of these recommendations.