Discriminating Power of FISWG Characteristic Descriptors Under Different Forensic Use Cases

FISWG characteristic descriptors are facial features that can be used for evidence evaluation during forensic case work. In this paper we investigate the discriminating power of a biometric system that uses these characteristic descriptors as features under different forensic use cases. We show that in every forensic use case we can find characteristic descriptors that exhibit moderate to low discriminating power. In all but one use cases, a commercial face recognition system outperforms the characteristic descriptors. However, in low resolution surveillance camera images, some (combination of) characteristic descriptors yield better results than commercial systems

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

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

Recent Advances in Forensic Anthropological Methods and Research

Forensic anthropology, while still relatively in its infancy compared to other forensic science disciplines, adopts a wide array of methods from many disciplines for human skeletal identification in medico-legal and humanitarian contexts. The human skeleton is a dynamic tissue that can withstand the ravages of time given the right environment and may be the only remaining evidence left in a forensic case whether a week or decades old. Improved understanding of the intrinsic and extrinsic factors that modulate skeletal tissues allows researchers and practitioners to improve the accuracy and precision of identification methods ranging from establishing a biological profile such as estimating age-at-death, and population affinity, estimating time-since-death, using isotopes for geolocation of unidentified decedents, radiology for personal identification, histology to assess a live birth, to assessing traumatic injuries and so much more

Expertise in Applied Face Matching: Training, Forensic Examiners, Super Matchers and Algorithms

Face matching is widely used in applied settings, including policing and border control, to identify persons of interest, where the consequences of an incorrect decision can have profound consequences. It is, therefore, of paramount importance that applied face- matching systems are accurate and reliable. However, humans are generally poor at matching face of people they don’t know, with large individual differences in accuracy. The aim of this thesis was to evaluate different sources of face-matching expertise (training, forensic face examination, superior face matchers and algorithms) and provide recommendations for how to improve face-matching performance in applied settings. Study one presents a survey of face-matching training, providing insights into the diverse and inconsistent approaches organisations use to train face-matching operators. The second study evaluates a two-day professional face-matching training course, demonstrating the limitations of short courses and the risk of introducing a match bias in low performers. In study three the perceptual skill of superior face matchers and forensic face examiners were compared, showing that by combining the selection of high performers with a wisdom of crowds approach, comparable levels of performance to trained examiners can be achieved in quick-decision face matching. Study four investigated the fusion of human face-matching decisions and algorithm similarity scores for faces that were challenging to humans and to the algorithm, highlighting the effectiveness of fusion in improving face-matching performance. Study five compared the operational accuracy of individual examiners and examiner teams on a face-matching task. Teams achieved higher levels of performance than individuals, with performance improving for both groups after fusion with a facial recognition algorithm. The thesis concludes with a discussion of how different sources of face-matching expertise can be used and combined in applied face-matching systems, and highlights areas for further research that would benefit the applied face-matching community

An Investigation into the Performance of Ethnicity Verification Between Humans and Machine Learning Algorithms

There has been a significant increase in the interest for the task of classifying demographic profiles i.e. race and ethnicity. Ethnicity is a significant human characteristic and applying facial image data for the discrimination of ethnicity is integral to face-related biometric systems. Given the diversity in the application of ethnicity-specific information such as face recognition and iris recognition, and the availability of image datasets for more commonly available human populations, i.e. Caucasian, African-American, Asians, and South-Asian Indians. A gap has been identified for the development of a system which analyses the full-face and its individual feature-components (eyes, nose and mouth), for the Pakistani ethnic group. An efficient system is proposed for the verification of the Pakistani ethnicity, which incorporates a two-tier (computer vs human) approach. Firstly, hand-crafted features were used to ascertain the descriptive nature of a frontal-image and facial profile, for the Pakistani ethnicity. A total of 26 facial landmarks were selected (16 frontal and 10 for the profile) and by incorporating 2 models for redundant information removal, and a linear classifier for the binary task. The experimental results concluded that the facial profile image of a Pakistani face is distinct amongst other ethnicities. However, the methodology consisted of limitations for example, low performance accuracy, the laborious nature of manual data i.e. facial landmark, annotation, and the small facial image dataset. To make the system more accurate and robust, Deep Learning models are employed for ethnicity classification. Various state-of-the-art Deep models are trained on a range of facial image conditions, i.e. full face and partial-face images, plus standalone feature components such as the nose and mouth. Since ethnicity is pertinent to the research, a novel facial image database entitled Pakistani Face Database (PFDB), was created using a criterion-specific selection process, to ensure assurance in each of the assigned class-memberships, i.e. Pakistani and Non-Pakistani. Comparative analysis between 6 Deep Learning models was carried out on augmented image datasets, and the analysis demonstrates that Deep Learning yields better performance accuracy compared to low-level features. The human phase of the ethnicity classification framework tested the discrimination ability of novice Pakistani and Non-Pakistani participants, using a computerised ethnicity task. The results suggest that humans are better at discriminating between Pakistani and Non-Pakistani full face images, relative to individual face-feature components (eyes, nose, mouth), struggling the most with the nose, when making judgements of ethnicity. To understand the effects of display conditions on ethnicity discrimination accuracy, two conditions were tested; (i) Two-Alternative Forced Choice (2-AFC) and (ii) Single image procedure. The results concluded that participants perform significantly better in trials where the target (Pakistani) image is shown alongside a distractor (Non-Pakistani) image. To conclude the proposed framework, directions for future study are suggested to advance the current understanding of image based ethnicity verification.Acumé Forensi