Forensic face recognition as a means to determine strength of evidence: A survey (original) (raw)
Related papers
Forensic Face Recognition: A Survey
2010
Beside a few papers which focus on the forensic aspects of automatic face recognition, there is not much published about it in contrast to the literature on developing new techniques and methodologies for biometric face recognition. In this report, we review forensic facial identification which is the forensic experts" way of manual facial comparison. Then we review famous works in the domain of forensic face recognition. Some of these papers describe general trends in forensics [1], guidelines for manual forensic facial comparison and training of face examiners who will be required to verify the outcome of automatic forensic face recognition system . Some proposes theoretical framework for application of face recognition technology in forensics [3] and automatic forensic facial comparison . Bayesian framework is discussed in detail and it is elaborated how it can be adapted to forensic face recognition. Several issues related with court admissibility and reliability of system are also discussed. Until now, there is no operational system available which automatically compare image of a suspect with mugshot database and provide result usable in court. The fact that biometric face recognition can in most cases be used for forensic purpose is true but the issues related to integration of technology with legal system of court still remain to be solved. There is a great need for research which is multidisciplinary in nature and which will integrate the face recognition technology with existing legal systems. In this report we present a review of the existing literature in this domain and discuss various aspects and requirements for forensic face recognition systems particularly focusing on Bayesian framework.
A Forensic Without the Science: Face Recognition in U.S. Criminal Investigations
Georgetown Law, 2022
The report examines the myriad human and machine factors, and their interactions, that might lead to bias and error when law enforcement agencies use face recognition. As a biometric, forensic investigative tool, face recognition may be particularly prone to errors arising from subjective human judgment, cognitive bias, low-quality or manipulated evidence, and under-performing technology. These errors have real-world consequences — the investigation and arrest of an unknown number of innocent people and the deprivation of due process of many, many more.
Security Journal, 2016
Following the deployment of strong biometric systems in forensic science (for example, finger/palmprints or DNA), additional weaker biometric data such as facial information, ear or gait, are making their way into police practices and judicial systems. Their introduction is not going without presenting new challenges because of their lower discrimination power (that is, their efficiency at distinguishing individuals). Current biometric systems are designed and deployed as stand-alone applications (operating on their own merit, detached from any other investigative information) and are not fit for purpose when dealing with less discriminating modalities such as faces. We posit in this article that, for these emergent modalities, a different framework, integrated with the policing strategy, is required. The proposed framework is designed to maximize the payoff of these modalities for investigation or intelligence purposes. The number of facial images of non-identified individuals of interest available to police forces is increasing. Their sources go from surveillance cameras, cameras from automated teller machine, personal devices and so on. We analyzed, between 2009 and 2013, data from a regional intelligence platform, used by the crime intelligence units and, show using real case examples, the potential of facial images for crime investigation and crime intelligence.
Biometrics in Forensic Identification: Applications and Challenges
Accurate and efficient identification have become a vital requirement for forensic application due to diversities of criminal activities. A recent advancement in biometric technology which is equipped with computational intelligence techniques is replacing manual identification approaches in forensic science. Biometrics is a fundamental verification mechanism that identifies individuals on the basis of their physiological and behavioral features. These biometric expansions are easily observable in different forensic identification areas, e.g. face, fingerprint, iris, voice, handwriting, etc. The effectiveness of biometrics system lies in different recognition processes which include feature extraction, feature robustness and feature matching. The emergence of forensic biometrics covers a wide range of applications for physical and cybercrime detection. Forensic Biometrics also overcomes the loopholes of traditional identification system that were based on personal probabilities. It is considered as a fundamental shift in the way criminals are detected. The present study describes the contribution and limitations of biometric science in the field of forensic identification.
Biometrics in Forensic Science: Challenges, Lessons and New Technologies
Lecture Notes in Computer Science, 2014
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 crimescenes (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. 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. 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.
Digital Forensics Face Detection and Recognition
International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2023
Face recognition is a biometric system used to identify or verify a person who appear in a scene often captured by security cameras like at Airports, Offices, Universities, Banks, etc. In this project, we have built a Face detection and Recognition model over images and videos. We have used Viola Jones Algorithm to detect a face(s) in an digital image and Convolutional Neural Network to predict it, for identification or verification. We have extended this model to be able to detect and predict faces which are partially covered with glasses, sunglasses, mask and facemask. We have also used data augmentation to increase the dataset size which in result has boosted the performance of the model.
21 Linkages between Biometrics and Forensic Science
2007
Using biometric data for classification and/or identification in forensic science dates back to the turn of the 20 century. Biometrics as we know it today can be viewed as extension of Bertillon’s anthropometric approach, benefiting from automation and the use of additional features. This chapter presents a historical and technical overview of the development and the evolution of forensic biometric systems, used initially manually and then in a semi-automatic way. Before focusing on specific forensic fields, we will define the area, its terminology and draw distinctions between forensic science and biometrics. Forensic science refers to the applications of scientific principles and technical methods to an investigation in relation to criminal activities, in order to establish the existence of a crime, to determine the identity of its perpetrator(s) and their modus operandi. It is thus logical that this area was a fertile ground for the use of physiological or behavioral data to sort...
2020
Criminal law’s efficient and accurate administration depends to a considerable extent on the ability of decision-makers to identify unique individuals, circumstances and events as instances of abstract terms (such as events raising ‘reasonable suspicion’) laid out in the legal framework. Automated Facial Recognition has the potential to revolutionise the identification process, facilitate crime detection, and eliminate misidentification of suspects. This paper commences from the recent decision regarding the deployment of AFR by South Wales Police in order to discuss the lack of underpinning conceptual framework pertinent to a broader consideration of AFR in other contexts. We conclude that the judgment does not give the green light to other fact sensitive deployments of AFR. We consider two of these: a) use of AFR as a trigger for intervention short of arrest; b) use of AFR in an evidential context in criminal proceedings. AFR may on the face of it appear objective and sufficient, but this is belied by the probabilistic nature of the output, and the building of certain values into the tool, raising questions as to the justifiability of regarding the tool’s output as an ‘objective’ ground for reasonable suspicion. The means by which the identification took place must be disclosed to the defence, if Article 6 right to a fair trial is to be upheld, together with information regarding disregarded ‘matches’ and error rates and uncertainties of the system itself. Furthermore, AFR raises the risk that scientific or algorithmic findings could usurp the role of the legitimate decision-maker, necessitating the development of a framework to protect the position of the human with decision-making prerogative.
Face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms
Proceedings of the National Academy of Sciences of the United States of America, 2018
Achieving the upper limits of face identification accuracy in forensic applications can minimize errors that have profound social and personal consequences. Although forensic examiners identify faces in these applications, systematic tests of their accuracy are rare. How can we achieve the most accurate face identification: using people and/or machines working alone or in collaboration? In a comprehensive comparison of face identification by humans and computers, we found that forensic facial examiners, facial reviewers, and superrecognizers were more accurate than fingerprint examiners and students on a challenging face identification test. Individual performance on the test varied widely. On the same test, four deep convolutional neural networks (DCNNs), developed between 2015 and 2017, identified faces within the range of human accuracy. Accuracy of the algorithms increased steadily over time, with the most recent DCNN scoring above the median of the forensic facial examiners. Us...
Loading Preview
Sorry, preview is currently unavailable. You can download the paper by clicking the button above.
Related papers
van Tilorg, H.C.A, and Jajodia, S. (eds.), Encyclopedia of Cryptography and Security (2nd ed.), New York: Springer, pp. 130-4. ISBN 978-1-4419-5905-8 doi: 10.1007/978-1-4419-5906-5_731 , 2011
Bruinsma, G. and Weisburd, D. (eds), Encyclopedia of Criminology and Criminal Justice, New York: Springer, p. 1713-29. ISBN 978-1-4614-5689-6 (Print), 978-1-4614-5690-2 (Online) doi: 10.1007/978-1-4614-5690-2_170, 2014