24,168 resources related to Face Recognition
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The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.
The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.
Multimedia technologies, systems and applications for both research and development of communications, circuits and systems, computer, and signal processing communities.
The ICASSP meeting is the world's largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class speakers, tutorials, exhibits, and over 50 lecture and poster sessions.
2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.
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; ...
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-- ...
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.
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. ...
The design and manufacture of consumer electronics products, components, and related activities, particularly those used for entertainment, leisure, and educational purposes
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 ...
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 ...
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 ...
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 ...
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, ...
Honors 2020: Ramalingam Chellappa Wins the Jack S. Kilby Signal Processing Medal
Developing Point-of-Care Technologies
The IEEE in 2030: 11 May 2016
26th Annual MTT-AP Symposium and Mini Show - Opening Remarks and Dr. Murthy Upmaka
Your Digital Clone for Games, Videos, and More
2012 IEEE Honors - Corporate Innovation Recognition
Citing Sources Appropriately
2011 IEEE Awards Corporate Innovation Recognition - imec
Low Power Image Recognition: The Challenge Continues
Coming Soon: Brain Fuel 2017
2011 IEEE Awards Ernst Weber Engineering Leadership Recognition - Tze-Chiang Chen
2012 IEEE Honors - IEEE Ernst Weber Engineering Leadership Recognition
What should we reboot next? - Cliff Young Keynote - ICRC San Mateo, 2019
IEEE Low-Power Image Recognition Challenge (LPIRC)
Welcome: Low Power Image Recognition Challenge
2011 IEEE Awards Matt Ettus HKN Eminent Member Recognition
IEEE in China (Member Access)
Resistive Coupled VO2 Oscillators for Image Recognition - Elisabetta Corti - ICRC 2018
MGA Session 2: Direction and the Section Leader (Member Access)
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 static test of algorithm, but also carry out the dynamic face recognition test of actual faces. At the same time, considering the influence of hardware configuration, hardware configuration parameters of face recognition products or systems should be paid more attention. In the future, the development trend of evaluating face-recognition technology will become both static test in algorithm level and dynamic test of recognition effect to actual faces in application level should be carried out. Even the videotaped face-recognition test and system hardware configuration check should be carried out simultaneously.
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 small number of studies which compare both these approaches. There is a tremendous increase in face recognition research nowadays; primarily because of the various negative events taking place around the globe. With the increase in the number of proposed algorithms and techniques the survey and evaluation of these algorithms and techniques becomes more vital to provide a boost to the research activities. The primary aim of this paper is to provide a critical summary of the existing literature on human face recognition over the past decade with special reference to holistic approaches to face detection.
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 database of left lens face image and right lens face image. The use of two sides of the face angle taking is used to avoid falsification of facial data such as the use of a face photo of a person or an image similar to a person's face. This research uses a dual- vision face recognition method on its preprocessing and uses 3WPCA (Three Level Wavelet Decomposition - Principal Component Analysis) as its feature extraction model. In dual-vision face recognition, we use half-join method to combine a half of the left image and a half of the right image into an image that is ready to be extracted using 3WPCA. This research can produce a presence system based on good face recognition and can be used to anticipate falsification of face data with recognition accuracy up to 98%.
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 extract features that are invariant to illumination variation and parallel radial basis function neural networks to train different poses. The recognition performance of the proposed system is validated against the Yale B database and compared to other systems implemented on the same database.
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, recognition is a primary task and it's significant to achieve the task with face recognition; then, the algorithm and software structure of face recognition is investigated; finally, based on these, face recognition system based on humanoid robot is accomplished. Experimental results show that the system is feasible.
In 2010, National Institute of Standard and Technology (NIST) of the U.S. published “Report on the Evaluation of 2D Still-Image Face Recognition Algorithms (MBE 2010 Still Face).” The report mentions that there has been a remarkably huge improvement in the area of face recognition technology from the start of FERET (FacE REcognition Technology) program in 1993 up to 2010. While MBE 2010 Still Face is considered to be one of the best references in choosing appropriate face recognition algorithms from various kinds of software programs in the world, several points seem to be missing that need to be taken into consideration in the evaluation of recognition accuracy when face recognition technology is made use of in criminal investigations. They are the evaluation of the influence coming from (a) longer lapse of time (15-year aging difference), (b) shooting angles (vertical and horizontal), (c) change of face expression (smiling and laughing), and (d) accessories (cap, sunglass, mustache and so on). As the images taken by CCTVs on streets aren't always ideal mug shots, these points are also crucial in selecting the best face recognition algorithms as a tool to fight against crimes. Police Info- Communications Research Center (PICRC) attempts to evaluate the accuracy of face recognition technology by choosing some of the representative face recognition algorithms mentioned in MBE 2010 Still Face. PICRC has certain image database that stores two groups of full-faced photographs of people taken at intervals of 15 years. For instance as for the evaluation of the point (a), after the representative face recognition algorithms compared the photographs of the people with those of their former selves already stored in the database step by step, the degree of face recognition accuracy were verified. It is confirmed that the latest face recognition algorithms are hardly influenced by the four points ((a)-(d)) mentioned above. This result can conclude that the analyses made in MBE 2010 Still Face should be reliable enough even for police organizations to choose suitable face recognition algorithms for criminal investigations.
To deal with the variations caused by age, an aging face recognition method Based on HMAX model, which motivated by a quantitative model of visual cortex, was proposed to achieve temporal invariance. First, each face image was normalized to a standard size. Second, the C1-S features, which preserve facial texture and shape information, were defined by facial key points and HMAX model to represent the face image with the high dimensional features. Then C1-S features are projected to a low dimensional subspace by PCA. Finally, the nearest neighbor rule with Mahalanobis distance was used to aging face recognition from rank 1 to rank 6. Experiments on the FG-NET database show that our proposed C1-S features are good at tolerating local position, scale and aging variations and improve the accuracy of aging face recognition.
Security is a major threat to institutions that is why there is a need of several specially trained personnel to attain the desired security to overcome the declining security conditions in the country. These personnel, as human beings, make mistakes that might affect the level of security. The need for facial recognition system that is fast and accurate is continuously increasing which can detect intruders and restricts them from restricted or high-security areas in real time and help in minimizing human error. Face recognition is one of the most important biometrics pattern recognition technique which is used in a broad spectrum of applications. The time and accuracy factor is considered as a major problem that specifies the performance of automatic face recognition system in real time environments. Various solutions have been proposed using multicore systems. However, harnessing current advancements is not without difficulties. Motivated by such challenge, this paper provides the architectural design, detailed design and proposes a comparative analysis for a Real Time Face Recognition System with three variant implementations of Real Time Face Recognition algorithms including Local Binary Patterns Histograms (LBP), PCA (Principal Component Analysis) and Fisher face. Finally, this paper concludes the speed obtained for the advanced implementations achieved by integrating embedded system models against the convention implementation.
Face recognition is a very important topic in the field of pattern recognition. Traditional two-dimensional face recognition technologies using images taken by a single camera are easily influenced by expressions and poses resulting in low recognition accuracy. In this paper, a new three-dimensional face recognition technique is proposed. We apply a dual camera module to extract two images of simulated human eyes. The active appearance model is applied to find facial feature points. The disparity between the images of the left eye and the right eye is calculated and used to reconstruct a 3D face model. Twenty-four geometric features are extracted from the 3D face models and a multi-class support vector machine is then applied to face recognition. The experimental results show that the proposed method can reduce the influence of facial expressions and the risk of photo fraud.
In the recent years, face recognition has obtained much attention. Using combined 2D and 3D face recognition is an alternative method to deal with face recognition. A novel multimodal face recognition algorithm based on Gabor wavelet information is presented in this paper. The Principal Component Analysis (PCA) and the Linear Discriminant analysis (LDA) have been used for size reduction. The system has combined 2D and 3D systems in the decision level which presents higher performance in contrast with methods which use only 2D and 3D systems, separately. The proposed algorithm is examined with FRAV3D database that has faces with pose variation and 95% performance that is achieved in rank-one for fusion experiment.
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