1,669 research outputs found
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
3D Face Reconstruction: the Road to Forensics
3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make its possible role in bringing evidence to a lawsuit unclear. An extensive investigation of the constraints, potential, and limits of its application in forensics is still missing. Shedding some light on this matter is the goal of the present survey, which starts by clarifying the relation between forensic applications and biometrics, with a focus on face recognition. Therefore, it provides an analysis of the achievements of 3D face reconstruction algorithms from surveillance videos and mugshot images and discusses the current obstacles that separate 3D face reconstruction from an active role in forensic applications. Finally, it examines the underlying data sets, with their advantages and limitations, while proposing alternatives that could substitute or complement them
3D Face Reconstruction: the Road to Forensics
3D face reconstruction algorithms from images and videos are applied to many
fields, from plastic surgery to the entertainment sector, thanks to their
advantageous features. However, when looking at forensic applications, 3D face
reconstruction must observe strict requirements that still make its possible
role in bringing evidence to a lawsuit unclear. An extensive investigation of
the constraints, potential, and limits of its application in forensics is still
missing. Shedding some light on this matter is the goal of the present survey,
which starts by clarifying the relation between forensic applications and
biometrics, with a focus on face recognition. Therefore, it provides an
analysis of the achievements of 3D face reconstruction algorithms from
surveillance videos and mugshot images and discusses the current obstacles that
separate 3D face reconstruction from an active role in forensic applications.
Finally, it examines the underlying data sets, with their advantages and
limitations, while proposing alternatives that could substitute or complement
them.Comment: The manuscript has been accepted for publication in ACM Computing
Surveys. arXiv admin note: text overlap with arXiv:2303.1116
Assessing the Influencing Factors on the Accuracy of Underage Facial Age Estimation
Swift response to the detection of endangered minors is an ongoing concern
for law enforcement. Many child-focused investigations hinge on digital
evidence discovery and analysis. Automated age estimation techniques are needed
to aid in these investigations to expedite this evidence discovery process, and
decrease investigator exposure to traumatic material. Automated techniques also
show promise in decreasing the overflowing backlog of evidence obtained from
increasing numbers of devices and online services. A lack of sufficient
training data combined with natural human variance has been long hindering
accurate automated age estimation -- especially for underage subjects. This
paper presented a comprehensive evaluation of the performance of two cloud age
estimation services (Amazon Web Service's Rekognition service and Microsoft
Azure's Face API) against a dataset of over 21,800 underage subjects. The
objective of this work is to evaluate the influence that certain human
biometric factors, facial expressions, and image quality (i.e. blur, noise,
exposure and resolution) have on the outcome of automated age estimation
services. A thorough evaluation allows us to identify the most influential
factors to be overcome in future age estimation systems
Digital Forensics AI: Evaluating, Standardizing and Optimizing Digital Evidence Mining Techniques
The impact of AI on numerous sectors of our society and its successes over the years indicate that it can assist in resolving a variety of complex digital forensics investigative problems. Forensics analysis can make use of machine learning modelsâ pattern detection and recognition capabilities to uncover hidden evidence in digital artifacts that would have been missed if conducted manually. Numerous works have proposed ways for applying AI to digital forensics; nevertheless, scepticism regarding the opacity of AI has impeded the domainâs adequate formalization and standardization. We present three critical instruments necessary for the development of sound machine-driven digital forensics methodologies in this paper. We cover various methods for evaluating, standardizing, and optimizing techniques applicable to artificial intelligence models used in digital forensics. Additionally, we describe several applications of these instruments in digital forensics, emphasizing their strengths and weaknesses that may be critical to the methodsâ admissibility in a judicial process
Handbook of Digital Face Manipulation and Detection
This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area
Calibration of score based likelihood ratio estimation in automated forensic facial image comparison
Calibration of score based likelihood ratio estimation in automated forensic facial image comparison
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