Conferences related to Face Recognition

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2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Early Vision and Sensors Color, Illumination and Texture Segmentation and Grouping Motion and TrackingStereo and Structure from Motion Image -Based Modeling Physics -Based Modeling Statistical Methods and Learning in VisionVideo Surveillance and Monitoring Object, Event and Scene Recognition Vision - Based Graphics Image and Video RetrievalPerformance Evaluation Applications


2018 13th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

HRI is a highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchersin robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior,anthropology, and many other fields.

  • 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.

  • 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    The conference serves as the primary annual meeting for researchers in the field of human-robot interaction. The event will include a main papers track and additional sessions for posters, demos, and exhibits. Additionally, the conference program will include a full day of workshops and tutorials running in parallel.

  • 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    This conference focuses on the interaction between humans and robots.

  • 2015 10th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a single -track, highly selective annual conference that showcases the very bestresearch and thinking in human -robot interaction. HRI is inherently interdisciplinary and multidisciplinary,reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificialintelligence, organizational behavior, anthropology, and many other fields.

  • 2014 9th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a highly selective annual conference that showcases the very best research and thinking in human -robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.

  • 2013 8th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a single -track, highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.

  • 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    HRI is a single-track, highly selective annual conference that showcases the very best research and thinking in human-robot interaction. HRI is inherently interdisciplinary and multidisciplinary, reflecting work from researchers in robotics, psychology, cognitive science, HCI, human factors, artificial intelligence, organizational behavior, anthropology, and many other fields.

  • 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    Robot companions Lifelike robots Assistive (health & personal care) robotics Remote robots Mixed initiative interaction Multi-modal interaction Long-term interaction with robots Awareness and monitoring of humans Task allocation and coordination Autonomy and trust Robot-team learning User studies of HRI Experiments on HRI collaboration Ethnography and field studies HRI software architectures HRI foundations Metrics for teamwork HRI group dynamics.

  • 2010 5th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    TOPICS: Robot companions, Lifelike robots, Assistive (health & personal care) robotics, Remote robots, Mixed initiative interaction, Multi-modal interaction, Long-term interaction with robots, Awareness and monitoring of humans, Task allocation and coordination, Autonomy and trust, Robot-team learning, User studies of HRI, Experiments on HRI collaboration, Ethnography and field studies, HRI software architectures

  • 2009 4th ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    * Robot companions * Lifelike robots * Assistive (health & personal care) robotics * Remote robots * Mixed initiative interaction * Multi-modal interaction * Long-term interaction with robots * Awareness and monitoring of humans * Task allocation and coordination * Autonomy and trust * Robot-team learning * User studies of HRI * Experiments on HRI collaboration * Ethnography and field studies * HRI software architectures

  • 2008 3rd ACM/IEEE International Conference on Human-Robot Interaction (HRI)

    Robot companions Lifelike robots Assistive (health & personal care) robotics Remote robots Mixed initiative interaction Multi-modal interaction Long-term interaction with robots Awareness and monitoring of humans Task allocation and coordination Autonomy and trust Robot-team learning User studies of HRI Experiments on HRI collaboration Ethnography and field studies HRI software architectures HRI foundations Metrics for teamwork HRI group dynamics Individual vs. group HRI

  • 2007 2nd Annual Conference on Human-Robot Interaction (HRI)


2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)

Industrial Informatics, Computational Intelligence, Control and Systems, Cyber-physicalSystems, Energy and Environment, Mechatronics, Power Electronics, Signal and InformationProcessing, Network and Communication Technologies


2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018)

conference on automatic analysis, recognition, and applications of human face and body gesture

  • 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017)

    The IEEE conference series on Automatic Face and Gesture Recognition is the premier international forum for research in image and video-based face, gesture, and body movement recognition. Its broad scope includes: advances in fundamental computer vision, pattern recognition and computer graphics; machine learning techniques relevant to face, gesture, and body motion; new algorithms and applications. The conference presents research that advances the state-of-the-art in these and related areas, leading to new capabilities in various application domains.

  • 2015 IEEE 11th International Conference on Automatic Face & Gesture Recognition (FG 2015)

    The IEEE conference series on Automatic Face and Gesture Recognition is the premier international forum for research in image and video-based face, gesture, and body movement recognition. Its broad scope includes: advances in fundamental computer vision, pattern recognition and computer graphics; machine learning techniques relevant to face, gesture, and body motion; new algorithms and applications. The conference presents research that advances the state-of-the-art in these and related areas, leading to new capabilities in various application domains.

  • 2013 10th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2013)

    The IEEE conference on Automatic Face and Gesture Recognition is the premier international forum for research in image and video- based face, gesture, and body movement recognition. Its broad scope includes advances in fundamental computer vision, pattern recognition, computer graphics, and machine learning techniques relevant to face, gesture, and body action, new algorithms, and analysis of specific applications. The program will be single- track with poster sessions. Submissions will be rigorously reviewed and should clearly make the case for a documented improvement over the existing state of the art.

  • 2011 IEEE International Conference on Automatic Face & Gesture Recognition (FG 2011)

    FG is the premier international forum for research and technology advances in image and video-based detection, modeling, and recognition of human faces and activity.

  • 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2008)

    The IEEE conference series on Automatic Face and Gesture Recognition is the premier international forum for state of the art image and video-based biometric gesture and body movement recognition including face Recognition/Analysis (tracking/detection, recognition, expression analysis, 3D analysis) gesture Recognition/Analysis (gesture interpretation, head tracking, arm/limb and body analysis/tracking), Body Motion Analysis (human motion analysis, gait recognition, 3d movement and gait analysis), etc.

  • 2006 7th International Conference on Automatic Face & Gesture Recognition (FG 2006)


2018 14th IEEE International Conference on Signal Processing (ICSP)

ICSP2018 includes sessions on all aspects of theory, design and applications of signal processing. Prospective authors are invited to propose papers in any of the following areas, but not limited to: A. Digital Signal Processing (DSP)B. Spectrum Estimation & ModelingC. TF Spectrum Analysis & WaveletD. Higher Order Spectral AnalysisE. Adaptive Filtering &SPF. Array Signal ProcessingG. Hardware Implementation for Signal ProcessingH Speech and Audio CodingI. Speech Synthesis & RecognitionJ. Image Processing & UnderstandingK. PDE for Image ProcessingL.Video compression &StreamingM. Computer Vision & VRN. Multimedia & Human-computer InteractionO. Statistic Learning & Pattern RecognitionP. AI & Neural NetworksQ. Communication Signal processingR. SP for Internet and Wireless CommunicationsS. Biometrics & AuthentificationT. SP for Bio-medical & Cognitive ScienceU


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Periodicals related to Face Recognition

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Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


Circuits and Systems II: Express Briefs, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.


Computing in Science & Engineering

Physics, medicine, astronomy—these and other hard sciences share a common need for efficient algorithms, system software, and computer architecture to address large computational problems. And yet, useful advances in computational techniques that could benefit many researchers are rarely shared. To meet that need, Computing in Science & Engineering (CiSE) presents scientific and computational contributions in a clear and accessible format. ...


Consumer Electronics, IEEE Transactions on

The design and manufacture of consumer electronics products, components, and related activities, particularly those used for entertainment, leisure, and educational purposes


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Most published Xplore authors for Face Recognition

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Xplore Articles related to Face Recognition

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The development trend of evaluating face-recognition technology

[{u'author_order': 1, u'affiliation': u'The Testing Center, The Third Research Institute of the Ministry of Public Security, Shanghai, China', u'full_name': u'Caixia Liu'}] 2014 International Conference on Mechatronics and Control (ICMC), 2014

In practical application, the result of face recognition not only depends on the static face recognition algorithm, but also depends on the dynamic face recognition algorithm. In a face recognition system, face image acquisition equipment and algorithm processor hardware will also affect speed and effect of the recognition. Therefore, when evaluating face-recognition technology, we should not only carry out the ...


Face recognition: A holistic approach review

[{u'author_order': 1, u'affiliation': u'Department of MCA, Millennium Institute of Management, Aurangabad, India', u'full_name': u'Ghazi Mohammed Zafaruddin'}, {u'author_order': 2, u'affiliation': u'Department of Computational Sciences, Swami Ramanand Teerth Marathwada University, Nanded, India', u'full_name': u'H. S. Fadewar'}] 2014 International Conference on Contemporary Computing and Informatics (IC3I), 2014

Face recognition has become more significant and relevant in recent years owing to its potential applications. Face recognition has far reaching benefits to corporations, the government and the greater society. Face recognition is basically identifying individuals by their faces. There are many face recognition approaches which are generally classified as feature based and holistic approaches. Presently there are a very ...


Anti-cheating presence system based on 3WPCA-dual vision face recognition

[{u'author_order': 1, u'affiliation': u'Faculty of Information Technology, Universitas Stikubank Semarang Indonesia', u'full_name': u'Edy Winarno'}, {u'author_order': 2, u'affiliation': u'Faculty of Information Technology, Universitas Stikubank Semarang Indonesia', u'full_name': u'Wiwien Hadikurniawati'}, {u'author_order': 3, u'affiliation': u'Faculty of Information Technology, Universitas Stikubank Semarang Indonesia', u'full_name': u'Imam Husni Al Amin'}, {u'author_order': 4, u'affiliation': u'Faculty of Information Technology, Universitas Stikubank Semarang Indonesia', u'full_name': u'Muji Sukur'}] 2017 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2017

To prevent counterfeit face image on face presence system, we can use dual vision camera in face recognition system. Dual vision camera is used to produce detectable face images from two positions of the left lens and the right lens. Image retrieval at the two corners of the left lens and the right lens can produce a merged face image ...


Multiview-multiband face recognition system to solve illumination and pose variation

[{u'author_order': 1, u'affiliation': u'Department of Electrical and Electronic Engineering, University of Nottingham Malaysia Campus, Malaysia', u'full_name': u"Sue Inn Ch'ng"}, {u'author_order': 2, u'affiliation': u'Department of Electrical and Electronic Engineering, University of Nottingham Malaysia Campus, Malaysia', u'full_name': u'Kah-Phooi Seng'}, {u'author_order': 3, u'affiliation': u'Department of Electrical and Electronic Engineering, University of Nottingham Malaysia Campus, Malaysia', u'full_name': u'Li-Minn Ang'}] 2010 3rd International Conference on Computer Science and Information Technology, 2010

Identifying faces under the influence of illumination and pose can be challenging as the presence of two variations on the same image can greatly change the appearance of a person. Thus, in this paper, we propose a multiview face recognition system that is able to solve illumination and pose face recognition problems. The proposed system uses multiband feature technique to ...


Face recognition algorithm and application developed for humanoid robot

[{u'author_order': 1, u'affiliation': u'Department of Automation, Shanghai Jiaotong University, 200240, China', u'full_name': u'Chao Luo'}, {u'author_order': 2, u'affiliation': u'Department of Automation, Shanghai Jiaotong University, 200240, China', u'full_name': u'Jian-Bo Su'}] Proceedings of the 32nd Chinese Control Conference, 2013

A face recognition system based on humanoid robot is discussed and implemented in this paper; the structure and hardware features of humanoid robot NAO are analyzed, and the meaning and method of achieving face recognition system are discussed; a humanoid robot is a copy of human by science and technology, whose visual system is just like human's eyes, certainly, therefore, ...


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Educational Resources on Face Recognition

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eLearning

No eLearning Articles are currently tagged "Face Recognition"

IEEE-USA E-Books

  • Multimodal Biometrics Based on NearInfrared Face Recognition

    This chapter contains sections titled: * Introduction * NIR Face-Based Multibiometrics * Method of Multibiometrics Fusion * Experiments * Conclusions * Acknowledgments * References ]]>

  • Learning Facial Aging Models: A Face Recognition Perspective

    This chapter contains sections titled: * Introduction * Age Progression during Formative Years * Discussions and Conclusions * References ]]>

  • Combining Geometrical and Statistical Models for VideoBased Face Recognition

    This chapter contains sections titled: * Introduction * Method for Learning GAMs * Robust and Efficient Tracking on GAMs * Face Recognition from Video * Experiment Results * Conclusion * References ]]>

  • PersonSpecific Characteristic Feature Selection for Face Recognition

    This chapter contains sections titled: * Introduction * Face Recognition in Humans * Our Approach to Face Recognition * Feature Extractors * The Learning Algorithm * Methodology * Results * Conclusions and Future Work * References ]]>

  • Recognition of Humans and Their Activities Using Video

    The recognition of humans and their activities from video sequences is currently a very active area of research because of its applications in video surveillance, design of realistic entertainment systems, multimedia communications, and medical diagnosis. In this lecture, we discuss the use of face and gait signatures for human identification and recognition of human activities from video sequences. We survey existing work and describe some of the more well-known methods in these areas. We also describe our own research and outline future possibilities. In the area of face recognition, we start with the traditional methods for image-based analysis and then describe some of the more recent developments related to the use of video sequences, 3D models, and techniques for representing variations of illumination. We note that the main challenge facing researchers in this area is the development of recognition strategies that are robust to changes due to pose, illumination, disguise, and aging. Gait recognition is a more recent area of research in video understanding, although it has been studied for a long time in psychophysics and kinesiology. The goal for video scientists working in this area is to automatically extract the parameters for representation of human gait. We describe some of the techniques that have been developed for this purpose, most of which are appearance based. We also highlight the challenges involved in dealing with changes in viewpoint and propose methods based on image synthesis, visual hull, and 3D models. In the domain of human activity recognition, we present an extensive survey of various methods that have been developed in different disciplines like artificial intelligence, image processing, pattern recognition, and computer vision. We then outline our method for modeling complex activities using 2D and 3D deformable shape theory. The wide application of automatic human identification and activity recognition methods will require the fusion of different modalities like face and gait, dealing with the problems of pose and illumination variations, and accurate computation of 3D models. The last chapter of this lecture deals with these areas of future research.

  • Dictionary Learning in Visual Computing

    The last few years have witnessed fast development on dictionary learning approaches for a set of visual computing tasks, largely due to their utilization in developing new techniques based on sparse representation. Compared with conventional techniques employing manually defined dictionaries, such as Fourier Transform and Wavelet Transform, dictionary learning aims at obtaining a dictionary adaptively from the data so as to support optimal sparse representation of the data. In contrast to conventional clustering algorithms like K-means, where a data point is associated with only one cluster center, in a dictionary-based representation, a data point can be associated with a small set of dictionary atoms. Thus, dictionary learning provides a more flexible representation of data and may have the potential to capture more relevant features from the original feature space of the data. One of the early algorithms for dictionary learning is K-SVD. In recent years, many variations/extensions of K-SVD and other new algorithms have been proposed, with some aiming at adding discriminative capability to the dictionary, and some attempting to model the relationship of multiple dictionaries. One prominent application of dictionary learning is in the general field of visual computing, where long-standing challenges have seen promising new solutions based on sparse representation with learned dictionaries. With a timely review of recent advances of dictionary learning in visual computing, covering the most recent literature with an emphasis on papers after 2008, this book provides a systematic presentation of the general methodologies, specific algorithms, and examples of applications for those who wish to have a quick start on this subject.

  • On Forensic Use of Biometrics

    Forensic science largely concerns the analysis of crime. The science of biometrics has developed approaches that are used to automatically identify individuals by personal characteristics. Biometric techniques have primarily been used to assure identity. The main steps of a biometric recognition approach include: acquisition of the biometric data, localization and alignment of the data, feature extraction, and matching. This chapter concentrates on two case studies discussing the forensic possibilities of face and ear as biometrics. It introduces the manual and computer-aided forensic face recognition. The chapter discusses the disparities between the behaviour of the current automatic face recognition systems and that which is needed for forensic application, and outlines the current progress towards addressing the challenges existing in face recognition. An emerging biometric ear is examined. There is a rich variety of approaches for ear biometrics and these are steeped in pattern recognition and computer vision.

  • Quaternionic Fuzzy Neural Network for View-Invariant Color Face Image Recognition

    This chapter contains sections titled: * Introduction * Face Recognition System * Quaternion-Based View-Invariant Color Face Image Recognition * Enrollment Stage and Recognition Stage for Quaternion-Based Color Face Image Correlator * Max-Product Fuzzy Neural Network Classifier * Experimental Results * Conclusion and Future Research Directions

  • Domain Adaptation for Visual Recognition

    Domain adaptation is an active, emerging research area that attempts to address the changes in data distribution across training and testing datasets. With the availability of a multitude of image acquisition sensors, variations due to illumination and viewpoint among others, computer vision applications present a very natural test bed for evaluating domain adaptation methods. This monograph provides a comprehensive overview of domain adaptation solutions for visual recognition problems. By starting with the problem description and illustrations, it discusses three adaptation scenarios, namely, (i) unsupervised adaptation where the "source domain" training data is partially labeled and the "target domain" test data is unlabeled; (ii) semi-supervised adaptation where the target domain also has partial labels; and (iii) multi- domain heterogeneous adaptation which studies the previous two settings with the source and/or target having more than one domain, and accounts for cases where the features used to represent the data in each domain are different. For all of these scenarios, Domain Adaptation for Visual Recognition discusses the existing adaptation techniques in the literature. These techniques are motivated by the principles of max-margin discriminative learning, manifold learning, sparse coding, as well as low-rank representations, and have shown improved performance on a variety of applications such as object recognition, face recognition, activity analysis, concept classification, and person detection. Domain Adaptation for Visual Recognition concludes by analyzing the challenges posed by the realm of "big visual data" -- in terms of the generalization ability of adaptation algorithms to unconstrained data acquisition as well as issues related to their computational tractability -- and draws parallels with efforts from the vision community on image transformation models and invariant descriptors so as to facilitate improved understanding of vision problems under uncertainty.

  • Kernel Methods in Computer Vision

    Few developments have influenced the field of computer vision in the last decade more than the introduction of statistical machine learning techniques. Particularly kernel-based classifiers, such as the support vector machine, have become indispensable tools, providing a unified framework for solving a wide range of image-related prediction tasks, including face recognition, object detection and action classification. By emphasizing the geometric intuition that all kernel methods rely on, Kernel Methods in Computer Vision provides an introduction to kernel-based machine learning techniques accessible to a wide audience including students, researchers and practitioners alike, without sacrificing mathematical correctness. It covers not only support vector machines but also less known techniques for kernel- based regression, outlier detection, clustering and dimensionality reduction. Additionally, it offers an outlook on recent developments in kernel methods that have not yet made it into the regular textbooks: structured prediction, dependency estimation and learning of the kernel function. Each topic is illustrated with examples of successful application in the computer vision literature, making Kernel Methods in Computer Vision a useful guide not only for those wanting to understand the working principles of kernel methods, but also for anyone wanting to apply them to real-life problems.



Standards related to Face Recognition

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Jobs related to Face Recognition

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