1,282 research outputs found
A Survey on Computer Vision based Human Analysis in the COVID-19 Era
The emergence of COVID-19 has had a global and profound impact, not only on
society as a whole, but also on the lives of individuals. Various prevention
measures were introduced around the world to limit the transmission of the
disease, including face masks, mandates for social distancing and regular
disinfection in public spaces, and the use of screening applications. These
developments also triggered the need for novel and improved computer vision
techniques capable of (i) providing support to the prevention measures through
an automated analysis of visual data, on the one hand, and (ii) facilitating
normal operation of existing vision-based services, such as biometric
authentication schemes, on the other. Especially important here, are computer
vision techniques that focus on the analysis of people and faces in visual data
and have been affected the most by the partial occlusions introduced by the
mandates for facial masks. Such computer vision based human analysis techniques
include face and face-mask detection approaches, face recognition techniques,
crowd counting solutions, age and expression estimation procedures, models for
detecting face-hand interactions and many others, and have seen considerable
attention over recent years. The goal of this survey is to provide an
introduction to the problems induced by COVID-19 into such research and to
present a comprehensive review of the work done in the computer vision based
human analysis field. Particular attention is paid to the impact of facial
masks on the performance of various methods and recent solutions to mitigate
this problem. Additionally, a detailed review of existing datasets useful for
the development and evaluation of methods for COVID-19 related applications is
also provided. Finally, to help advance the field further, a discussion on the
main open challenges and future research direction is given.Comment: Submitted to Image and Vision Computing, 44 pages, 7 figure
Biometric Spoofing: A JRC Case Study in 3D Face Recognition
Based on newly available and affordable off-the-shelf 3D sensing, processing and printing technologies, the JRC has conducted a comprehensive study on the feasibility of spoofing 3D and 2.5D face recognition systems with low-cost self-manufactured models and presents in this report a systematic and rigorous evaluation of the real risk posed by such attacking approach which has been complemented by a test campaign. The work accomplished and presented in this report, covers theories, methodologies, state of the art techniques, evaluation databases and also aims at providing an outlook into the future of this extremely active field of research.JRC.G.6-Digital Citizen Securit
Biometric antispoofing methods: A survey in face recognition
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. J. Galbally, S. Marcel and J. Fierrez, "Biometric Antispoofing Methods", IEEE Access, vol.2, pp. 1530-1552, Dec. 2014In recent decades, we have witnessed the evolution of biometric technology from the rst
pioneering works in face and voice recognition to the current state of development wherein a wide spectrum
of highly accurate systems may be found, ranging from largely deployed modalities, such as ngerprint,
face, or iris, to more marginal ones, such as signature or hand. This path of technological evolution has
naturally led to a critical issue that has only started to be addressed recently: the resistance of this rapidly
emerging technology to external attacks and, in particular, to spoo ng. Spoo ng, referred to by the term
presentation attack in current standards, is a purely biometric vulnerability that is not shared with other
IT security solutions. It refers to the ability to fool a biometric system into recognizing an illegitimate user
as a genuine one by means of presenting a synthetic forged version of the original biometric trait to the sensor.
The entire biometric community, including researchers, developers, standardizing bodies, and vendors, has
thrown itself into the challenging task of proposing and developing ef cient protection methods against this
threat. The goal of this paper is to provide a comprehensive overview on the work that has been carried out
over the last decade in the emerging eld of antispoo ng, with special attention to the mature and largely
deployed face modality. The work covers theories, methodologies, state-of-the-art techniques, and evaluation
databases and also aims at providing an outlook into the future of this very active eld of research.This work was supported in part by the CAM under Project S2009/TIC-1485, in part by the Ministry of Economy and Competitiveness through the Bio-Shield Project under Grant TEC2012-34881, in part by the TABULA RASA Project under Grant FP7-ICT-257289, in part by the BEAT Project under Grant FP7-SEC-284989 through the European Union, and in part by the Cátedra Universidad Autónoma de Madrid-Telefónica
Transdisciplinary AI Observatory -- Retrospective Analyses and Future-Oriented Contradistinctions
In the last years, AI safety gained international recognition in the light of
heterogeneous safety-critical and ethical issues that risk overshadowing the
broad beneficial impacts of AI. In this context, the implementation of AI
observatory endeavors represents one key research direction. This paper
motivates the need for an inherently transdisciplinary AI observatory approach
integrating diverse retrospective and counterfactual views. We delineate aims
and limitations while providing hands-on-advice utilizing concrete practical
examples. Distinguishing between unintentionally and intentionally triggered AI
risks with diverse socio-psycho-technological impacts, we exemplify a
retrospective descriptive analysis followed by a retrospective counterfactual
risk analysis. Building on these AI observatory tools, we present near-term
transdisciplinary guidelines for AI safety. As further contribution, we discuss
differentiated and tailored long-term directions through the lens of two
disparate modern AI safety paradigms. For simplicity, we refer to these two
different paradigms with the terms artificial stupidity (AS) and eternal
creativity (EC) respectively. While both AS and EC acknowledge the need for a
hybrid cognitive-affective approach to AI safety and overlap with regard to
many short-term considerations, they differ fundamentally in the nature of
multiple envisaged long-term solution patterns. By compiling relevant
underlying contradistinctions, we aim to provide future-oriented incentives for
constructive dialectics in practical and theoretical AI safety research
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
A hybrid learning scheme towards authenticating hand-geometry using multi-modal features
Usage of hand geometry towards biometric-based authentication mechanism has been commercially practiced since last decade. However, there is a rising security problem being surfaced owing to the fluctuating features of hand-geometry during authentication mechanism. Review of existing research techniques exhibits the usage of singular features of hand-geometric along with sophisticated learning schemes where accuracy is accomplished at the higher cost of computational effort. Hence, the proposed study introduces a simplified analytical method which considers multi-modal features extracted from hand geometry which could further improve upon robust recognition system. For this purpose, the system considers implementing hybrid learning scheme using convolution neural network and Siamese algorithm where the former is used for feature extraction and latter is used for recognition of person on the basis of authenticated hand geometry. The main results show that proposed scheme offers 12.2% of improvement in accuracy compared to existing models exhibiting that with simpler amendment by inclusion of multi-modalities, accuracy can be significantly improve without computational burden
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