Biometrics in forensic science: challenges, lessons and new technologies

Biometrics has historically found its natural mate in Forensics. The first applications found in the literature and over cited so many times, are related to biometric measurements for the identification of multiple offenders from some of their biometric and anthropometric characteristics (tenprint cards) and individualization of offender from traces found on crime-scenes (e.g. fingermarks, earmarks, bitemarks, DNA). From sir Francis Galton, to the introduction of AFIS systems in the scientific laboratories of police departments, Biometrics and Forensics have been "dating" with alternate results and outcomes. As a matter of facts there are many technologies developed under the "Biometrics umbrella" which may be optimised to better impact several Forensic scenarios and criminal investigations. At the same time, there is an almost endless list of open problems and processes in Forensics which may benefit from the introduction of tailored Biometric technologies. Joining the two disciplines, on a proper scientific ground, may only result in the success for both fields, as well as a tangible benefit for the society. A number of Forensic processes may involve Biometric-related technologies, among them: Evidence evaluation, Forensic investigation, Forensic Intelligence, Surveillance, Forensic ID management and Verification.\ud The COST Action IC1106 funded by the European Commission, is trying to better understand how Biometric and Forensics synergies can be exploited within a pan-European scientific alliance which extends its scope to partners from USA, China and Australia.\ud Several results have been already accomplished pursuing research in this direction. Notably the studies in 2D and 3D face recognition have been gradually applied to the forensic investigation process. In this paper a few solutions will be presented to match 3D face shapes along with some experimental results

Toward an Ethics of AI Belief

Philosophical research in AI has hitherto largely focused on the ethics of AI. In this paper we, an ethicist of belief and a machine learning scientist, suggest that we need to pursue a novel area of philosophical research in AI - the epistemology of AI, and in particular an ethics of belief for AI, i.e., an ethics of AI belief. Here we take the ethics of belief, a field that has been defined in various ways, to refer to a sub-field within epistemology. This subfield is concerned with the study of possible moral, practical, and other non-alethic dimensions of belief. And in this paper, we will primarily be concerned with the normative question within the ethics of belief of what agents - both human and artificial - ought to believe, rather than with descriptive questions concerning whether certain beliefs meet various evaluative standards such as being true, being justified or warranted, constituting knowledge, and so on. We suggest four topics in extant work in the ethics of (human) belief that can be applied to an ethics of AI belief: doxastic wronging by AI; morally owed beliefs; pragmatic and moral encroachment on AI beliefs; and moral responsibility for AI beliefs. We also indicate an important nascent area of philosophical research in epistemic injustice and AI that has not yet been recognized as research in the ethics of AI belief, but which is so in virtue of concerning moral and practical dimensions of belief

Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future