56 Facial Recognition Research Groups to Watch

56 Facial Recognition Research Groups to Watch

 

The research of facial recognition has been a fascinating journey. It began in the 1960s with Woody Bledsoe, Helen Chan Wolf, and Charles Bisson who created programs to assist with basic face recognition. They were not fully automated back then, requiring the administrator to locate the key facial features such as the eyes, ears, nose, and mouth on the image being examined. The programs calculated distances and ratios to a common reference point which was then compared to set reference data.

Since those early days, many facial recognition research groups have examined various aspects of facial detections and recognition. By the 1970s Goldstein, Harmon, and Lesk were able to automate the recognition process by using 21 specific subjective markers, such as hair color and lip thickness.

Kirby and Sirovich's research in the late 1980s gave another leap forward to the nascent technology, by determining that less than one hundred values were required to accurately code a suitable aligned and normalized face.

I have found 56 locations where facial recognition research groups have been the vanguard of 21st-century research into facial analysis. Some of this research is now historic, although still freely available on the internet. In other cases, the research is ongoing, with capabilities and techniques being improved on all the time. Some of the research is very clearly focused on facial research. Some of the other studies have only a peripheral connection to the subject.

I have tried to include all facial recognition research groups whose work appears on the internet. If you know of a group that is missing from this article feel free to contact us at Kairos and I am happy to update this post.

 

1. Bogazici University, Perpetual Intelligence Laboratory (PILAB)

 

There are three research groups in the Perpetual Intelligence Laboratory: Image Processing and Computer Vision Group, Machine Learning Group, and Speech Processing Group. Research on face recognition and detection has been undertaken in the Computer Vision and Image Processing Group.

NOTE: The main researcher in these fields appears to have been Berk Gokberk. He continues his research as Assistant Professor at the MEF University, Computer Engineering Department in Turkey.

 

2. California Institute of Technology, Machine Learning and Instrument Autonomy (MLIA)

 

The Machine Learning and Instrument Autonomy (MLIA) Group creates software solutions to difficult problems requiring data mining, knowledge discovery, pattern recognition, and automated classification and clustering. The underlying emphasis is on building systems based on learning algorithms. Some of the older research papers relate to facial detection and recognition, such as Finding Faces in Cluttered Scenes Using Labeled Random Graph Matching, but more recent research has tended to focus on rockets and geology.

 

3. Carnegie Mellon University, including

 

a. CyLab

Dr. Marios Savvides, Director of the CyLab Biometrics Lab, and his team focus their work on improving the use of iris and face recognition in biometric authentication systems. Savvides has developed new algorithms for use in face recognition technology which enable cameras to capture 100 image frames per second, thereby increasing the likelihood of pinpointing useable face images. They have also developed systems for 2D-3D mapping of faces.

b. Vision and Autonomous Systems Center (VASC), The Robotics Institute

The Vision and Autonomous Systems Center (VASC) has over 100 faculty, students, staff and visitors working on computer vision, autonomous navigation, virtual reality, intelligent manipulation, space robotics, and related fields. It includes the Cohn-Kanade AU-Coded Facial Expression Database. There have been a number of research projects on face recognition.

 

4. Chinese Academy of Sciences, Visual Information Processing and Learning (VIPL) group, affiliated with the Institute of Computing Technology

 

The research areas of VIPL group include computer vision, pattern recognition, machine learning, human-computer interaction, and neural computation. Researchers in the group particularly focus on face detection and recognition, visual modeling and recognition, visual perception and coding, gesture and sign language recognition, object detection and event analysis for intelligent video surveillance, and multimedia analysis/retrieval.

 

5. Cognitive System for Cognitive Assistants

 

This project dates from Sept 2004 - Aug 2008, and the main goal of the project was to advance the science of cognitive systems through a multidisciplinary investigation of requirements, design options and trade-offs for human-like, autonomous, integrated, physical (eg. robot) systems, including requirements for architectures, for forms of representation, for perceptual mechanisms, for learning, planning, reasoning and motivation, for action and communication. Face recognition was only a small part of this project.

 

6. Colorado State University, Computer Vision Group

 

Since 2000, the Vision Group at CSU has carried out a series of projects relating to face recognition. The main projects are the 2011 Baseline Algorithms, FaceL: Facile Face Labeling (a complete interactive face recognition system that learns and then recognizes a few people in live video), and the CSU Face Identification Evaluation System.

 

7. Delft University, The Delft Image Quality Lab, Interactive Intelligence Group

 

The have provided the TUD Image Quality Database which contains subjective data for experiments on perceived ringing, eye tracking data of experiments performed on different image databases and with different tasks, and other data. It is part of the Interactive Intelligence group which aims to engineer empathy.

 

8. Drexel University, Laboratory for Theoretical & Computational Neuroscience

 

Their long-term goal is to investigate and understand the key issue of neural control of movement: how different cellular, network and systems neural mechanisms are integrated across multiple levels of organization to produce motor behavior and to adapt this behavior to various external and internal conditions. There is minimal connection to face recognition in some past research undertaken.

 

9. Face and Gesture Recognition Working Group

 

The Face and Gesture Recognition Working Group (FGnet) was the European working group on face and gesture recognition funded by the E.C.IST program. The objectives of FGnet were to of encourage development of a technology for face and gesture recognition. The network goals were:

1. to assist development face and gesture recognition technology

2. to create a set of foresight reports defining development roadmaps and future use scenarios for the technology in the medium (5-7 years) and long (10-20 years) term

3. to specify, develop and supply resources (eg image sets) supporting these scenarios

Most of the research appear to have occurred 1996-2004, and there is still quite some information about it on their site.

 

10. Facebook AI Research

 

Facebook AI Research (FAIR) is committed to advancing the field of machine intelligence and developing technologies that give people better ways to communicate. In the long term, they seek to understand intelligence and make intelligent machines.

Research at the lab covers the full spectrum of topics related to AI, and to deriving knowledge from data: theory, algorithms, applications, software infrastructure and hardware infrastructure.

Facebook AI researchers are expected to contribute to the research community through publications, open source software, participation in technical conferences and workshops, and through collaborations with colleagues in academia.

“Moments” is a standalone app that uses facial recognition technology developed by FAIR to help people privately organize and share their photos with friends.

 

11. George Mason University, Center for Distributed and Intelligent Computation

 

The Distributed and Intelligent Computation (DIC) Center focuses on the design, development, and implementation of distributed and intelligent systems. The scientific, engineering and technological focus is on the interface between computing, sensors, networks, and learning. These are for applications related to biometrics and forensics, C4I, edutainment (education, entertainment, graphics, and game technology), e-science and web services, high-performance computing, homeland security, human-computer interaction (HCI), mobile and wireless communications, simulation, virtual reality, and scientific visualization.

They believe their expertise in biometrics and forensics, with an emphasis on face recognition and performance evaluation, is recognized worldwide. Additional biometric efforts are focused on video surveillance and monitoring (VSAM).

 

12. Graz University of Technology, Learning Recognition & Surveilliance, Institute for Computer Graphics and Vision

 

The research at the Institute for Computer Graphics and Vision (ICG) is focused on computer graphics, visualization, medical computer vision, object recognition, object reconstruction, robotics, virtual reality and augmented reality.

Learning, Recognition, and Surveillance is a work-group at the ICG. The group's main scientific research interests in the computer vision are machine learning and object recognition / detection / tracking, where the main focus of application is visual surveillance.

 

13. Idiap Research Institute

 

One of Idiap's areas of focus is biometric person recognition. This refers to the process of automatically recognizing a person using distinguishing behavioral patterns (gait, signature, keyboard typing, lip movement, hand-grip) or physiological traits (face, voice, iris, fingerprint, hand geometry, electroencephalogram (EEG), electrocardiogram (ECG), ear shape, body odor, body salinity, vascular).

 

14. Imago Research Group

 

The ongoing research of the IMAGO Research Group focuses on: 3D digital preservation of cultural and natural assets (precise 3D modeling and printing, virtual museum); biometrics (newborn recognition, face and action recognition); and assistive technologies for people with disabilities.

 

15. International Association for Pattern Recognition

 

The International Association for Pattern Recognition (IAPR) is an international association of non-profit, scientific and professional organizations concerned with pattern recognition, computer vision, and image processing in a broad sense.

Areas of pattern recognition currently represented by technical committees are:

• Statistical Pattern Recognition

• Structural and Syntactical Pattern Recognition

• Neural Networks and Computational Intelligence

• Benchmarking and Software

• Special Hardware and Software Environments

• Remote Sensing and Mapping

• Machine Vision Applications

• Biomedical Applications

• Graphics Recognition

• Reading Systems

• Multimedia and Visual Information Systems

• Pattern Recognition in Astronomy and Astrophysics

• Signal Analysis for Machine Intelligence

• Graph Based Representations

• Algebraic and Discrete Mathematical Techniques

• Machine Learning and Data Mining

• Discrete Geometry

• Cultural Heritage Applications

• Bioinformatics

 

16. Israel Institute of Technology, Geometric Image Processing Laboratory, Computer Science Department

 

The Geometrical Image Processing Lab, founded in 1998 by Professor Ron Kimmel, conducts theoretical and applied research in geometrical image processing, three-dimensional data analysis, image and video manipulation using dictionaries and sparse representations.

 

17. Karlsruhe Institute of Technology, Facial Image Processing and Analysis Group (FIPA)

 

The Facial Image Processing and Analysis Group’s objective is to conduct research on automatic processing of facial images in order to build systems that can read the faces as humans do. The following research topics are covered by the group:

• Face detection

• Facial feature localization

• Face modeling

• Face recognition and verification

• Face re-identification

• Facial expression analysis

• Emotion classification

• Facial gesture recognition

• Age estimation

• Gender classification

 

18. Korea Advanced Institute of Science and Technology, Image and Video Systems Lab (IVY)

 

The Image and Video Systems Lab researches in the area of practical image/video systems which are now demanded in modern information technology.

One area of focus is Color Face Recognition. This includes:

• Color face recognition algorithms for low-resolution faces

• Investigation of mismatch effects on color face recognition algorithms

• Color face recognition algorithms using feature selection

• Development of color local texture features for color face recognition

 

19. Max Planck Institute for Biological Cybernetics, Human Perception, Cognition and Action Department

 

The long-standing goal of this group is to unravel the mechanisms underlying recognition and categorization of faces and objects. Their research focuses on two main aspects:

1. To investigate recognition and categorization in more naturalistic scenarios. They use a mix of modern and traditional methods. In many tests, faces and human figure stimuli are moving and/or shown in their natural size. In other experiments, they test face recognition by allowing observers to actively move around in virtual environments.

2. To investigate perception in other populations, such as those with different cultural backgrounds (e.g. Koreans) or lacking specific skills (e.g. prosopagnosics), and more generally to investigate the role of expertise and cultural background in face recognition.

 

20. Medical Research Council, Cognition and Brain Sciences Unit

 

The Cognition and Brain Sciences Unit (CBU) studies human cognition and the brain. They only just touch on facial recognition.

They investigate fundamental human cognitive processes such as attention, language, memory, and emotion. Computer models are built to explain these processes. They have developed new methods that have the potential to lead to breakthroughs in cognitive science and neuroscience

 

21. Michigan State University, Biometrics Research Group, Department of Computer Science and Engineering

 

Members of the Biometrics Research Group undertake a variety of projects in the biometrics field. Recent projects include:

• A Longitudinal Study of Automatic Face Recognition

• Demographic Attribute Estimation from Face Images

• Face Image Clustering

• Face Liveness

• Facial Recognition of Lemurs

• Face Retrieval

 

22. Microsoft Research, Visual Computing

 

The Visual Computing Group at Microsoft Research Asia focuses its research on

• Imaging and Photogrammetry

• Pattern Recognition and Statistical Learning

• Object Detection and Recognition, including face detection, alignment, and tagging, video-based face recognition, and sparsity-based robust face recognition

• Dynamical Vision

• Interactive and Internet Vision

 

23. MIT Media Laboratory, including

 

a. Affective Computing

Affective Computing is computing that relates to, arises from, or deliberately influences emotion or other affective phenomena.

MIT's Affective Computing research has included:

• designing new ways for people to communicate affective-cognitive states, for instance through creation of novel wearable sensors and new machine learning algorithms that analyze multimodal channels of information

• creating new techniques to assess frustration, stress, and mood, through natural interaction and conversation

• showing how computers can be more emotionally intelligent

• inventing technologies for improving self-awareness of affective state and its selective communication to others

• increasing understanding of affect and its influence personal health

• examining ethical issues in affective computing.

b. Camera Culture

The Camera Culture group builds new tools to better capture and share visual information. Although much of their research relates to photography there is research into such topics as Portable Retinal Imaging.

 

24. Mitsubishi Electric Research Laboratories

 

Mitsubishi Electric Research Laboratories undertake research in a wide range of areas. Some research topic covered are Face Recognition Using Boosted Local Features andSilhouette-Based 3D Face Shape Recovery.

 

25. Nanjing University, LAMDA (Learning and Mining from Data)

 

The main research interests of LAMDA include machine learning, data mining, pattern recognition, information retrieval, evolutionary computation, neural computation, and some other related areas. Their current research mainly involves: ensemble learning, semi-supervised and active learning, multi-instance and multi-label learning, cost-sensitive and class-imbalance learning, metric learning, dimensionality reduction and feature selection, structure learning and clustering, theoretical foundations of evolutionary computation, improving comprehensibility, content-based image retrieval, web search and mining, face recognition, computer-aided medical diagnosis and bioinformatics.

The paper "Face recognition from a single image per person: A survey" is listed in Patter Recognition Journal's Top 10 Most Cited Papers During the Past Five Years (2011).

 

26. National University of Singapore, NUS Face Group

 

The NUS Face Group is dedicated to the development of advanced computer vision and image processing systems. Their research is focused on two broad areas: the development of algorithms for face recognition, and generation of realistic looking, animated human face models. Their research is motivated by applications in computer graphics, computer vision, computer animation and machine learning.

 

27. National Institute of Standards and Technology (NIST), Information Technology Laboratory (ITL)

 

The Information Technology Laboratory (ITL) has the broad mission to promote U.S. innovation and industrial competitiveness by advancing measurement science, standards, and technology through research and development in information technology, mathematics, and statistics.

NIST biometric researchers Patrick Grother and Mei Ngan, produced a reportPerformance of Face Identification Algorithms in 2013 which analyzed the performance of products from 16 organizations. Researchers defined performance by recognition accuracy—how many times the software correctly identified the photo—and the time the algorithms took to match one photo against massive photo data sets. Overall the results from its 2013 test of facial recognition algorithms show that accuracy has improved up to 30 percent since 2010.

 

28. Ohio State University, Facial Expression, Ohio Supercomputer Center

 

A research group led by Aleix Martinez, Ph.D., associate professor of Electrical and Computer Engineering at The Ohio State University, is investigating several important groups of expressions, known as compound-emotion categories. Compound emotions are those that can be constructed by combining basic component categories to create new ones. For instance, happily surprised and angrily surprised are two distinct compound-emotion categories.

The results of this study will also be useful to design computer algorithms that can do recognition of emotions. Face recognition is of primary importance in many areas of computational intelligence – ranging from human-computer interaction to content-based retrieval.

 

29. Pattern Recognition and Image Processing Group

 

Pattern Recognition and Image Processing aims at the extraction of information from such data as that created by modern sensors, like digital and video cameras. Typical applications are tasks like autonomous navigation, the detection of anomalies in medical images or the prediction of an eruption of a volcano.

Simple tasks like the detection of human faces are already solved and solutions are commercially available in digital cameras. However, there is still a large number of complex tasks that require the system to incorporate knowledge to be efficiently used to enhance the recognition results and to give semantically appropriate interpretations. The Pattern Recognition and Image Processing Group undertakes research to improve our knowledge in this area.

 

30. The Rockefeller University, Laboratory of Computational Neuroscience

 

The Laboratory of Computational Neuroscience developed an information theory of vision that explains the processing in the retina and lateral geniculate nucleus in the spatial-temporal and chromatic domains and that predicts data pertaining to human color adaptation and contrast sensitivity in space and time. They also explore cortical organization, function and plasticity. Their second focus is sensory perception. They postulate how the brain represents objects, using eigenheads to determine how the brain resolves segmentation, shape from shading, structure from motion, and recognition.

 

31. Universidad Autonoma de Madrid, ATVS Biometric Recognition Group, Escuela Politecnica Superior

 

The Biometric Recognition Group - ATVS is devoted to research in the areas of biometrics, pattern recognition, image analysis, and speech and signal processing. It is based in Madrid, Spain. Specific areas of research are biometric recognition, speaker recognition, fingerprints, signatures, biometrics and security, forensic biometrics, and language recognition.

 

32. Universidad de Chile, Computational Vision Laboratory, Department of Electrical Engineering

 

The main research interest areas of the Computational Vision Laboratory are the development of new computational vision paradigms as well as the application of image processing, machine learning and computational intelligence techniques to the resolution of computer vision and pattern recognition problems.

Their current research application fields are:

• face analysis in images and videos (detection, tracking, alignment and recognition)

• computational detection of humans (skin detection, hand gesture recognition and pornography detection)

• object detection and recognition, scene understanding

• mobile robotics vision (self-localization, tracking, visual SLAM).

 

33. Universidad De La República Uruguay, Image Processing Group, Institute of Electrical Engineering, Engineering School

 

In the last decade, the Image Processing Group has been working on several projects in the field of biometrics. One of these is Aguará, which is a face recognition biometric system, integrated as a control access application.

 

34. Universidad Rey Juan Carlos, Face Recognition and Artificial Vision

 

The FRAV laboratory "Face Recognition and Artificial Vision Research Laboratory for Advanced Security", researches in the following areas:

• Computer vision for intelligent video-surveillance

• Computer vision for road safety

• Computer vision for airport security and biometrics

• Event-based ultra-fast vision (AER)

• Biometrics and computer vision laboratory

 

35. University of Basel, Graphics and Vision Research Group

 

Research by the Graphics and Vision Research Group concentrates on the problem of automated image understanding. They combine methods from machine learning, computer graphics, and computer vision to implement analysis-by-synthesis systems for an automated image perception.

 

36. University of Bologna, Biometric System Laboratory, Department of Computer Science and Engineering (DISI)

 

The main research efforts of the Biometric System Laboratory are devoted to fingerprint and face recognition and to performance evaluation of biometric systems. The site includes a large bibliography of relevant research undertaken by members of the group in the area of facial recognition.

 

37. University of British Columbia, Image and Signal Processing Laboratory

 

The research carried out in the Image & Signal Processing Group (ISPL) can be listed under three headings : 1D Signal Processing (brain signals and audio), 2D signal processing (images) and 3D signal processing (videos). Some of the applications include processing of television images (cable television and high definition television), face recognition, biometrics, analysis of medical images (mammography, cytometry, microscopy, computer tomography, X-ray), multimedia indexing, copyright detection and real-time video.

 

38. University of California, including

 

a. Four Eyes Lab, Department of Computer Science

The Four Eyes Lab's research focus is on the "four I's" of Imaging, Interaction, and Innovative Interfaces.

Among the projects that have taken place is "Detecting, Tracking and Aligning Faces in Real-Time".

b. Machine Perception Laboratory

The goal of the Machine Perception Laboratory is to develop systems that perceive and interact with humans in real time using natural communication channels. To this effect they are developing perceptual primitives to detect and track human faces and to recognize facial expressions. They are also developing algorithms for robots that develop and learn to interact with people on their own. Applications include personal robots, perceptive tutoring systems, and systems for clinical assessment, monitoring, and intervention.

c. Perceptual Science Laboratory

The Perceptual Science Laboratory is undertaking research in:

• Embodied conversational agents and speech science (our perception and understanding are influenced by a speaker’s face and accompanying gestures, as well as the actual sound of speech)

• Baldi (an accurate three-dimensional animated talking head appropriately aligned with either synthesized or natural speech)

• Client/server architecture system To implement multilingual agents

• The Fuzzy Logical Model of Perception (FLMP)

• Computer-assisted speech and language tutors

 

39. University of Geneva, Neurology and Imaging of Cognition (NIC) Lab

 

The Neurology and Imaging of Cognition (NIC) Lab focuses on studying the cerebral bases of cognition, perception, emotion, and consciousness. One section of research is on emotion and faces.

 

40. University of Glasgow, Face Research Lab

 

The Face Research Lab is run by Ben Jones and Lisa DeBruine and is based in the Institute of Neuroscience and Psychology at the University of Glasgow. They operate a website FaceResearch.org which allows you to participate in short online psychology experiments looking at the traits people find attractive in faces and voices.

 

41. University of Houston, Computational Biomedicine Lab

 

The ultimate goal of the Computational Biomedicine Lab is to allow computers to aid humans in taking full advantage of the multitude of data sources available through today's technology to extract relevant information in an unobtrusive, reliable, accurate, and timely manner.

Amongst the current areas of research are the following biometric studies:

• Face recognition

• Ear recognition

• Facial expression analysis

 

42. University of Illinois, Computer Vision and Robotics Laboratory

 

The Computer Vision and Robotics Laboratory studies a wide range of problems related to the acquisition, processing and understanding of digital images. Their research addresses fundamental questions in computer vision, image and signal processing, machine learning, as well as applications in real-world problems.

 

43. University of London, Queen Mary Vision Laboratory

 

Queen Mary Vision Laboratory focuses on dynamic scene analysis, statistical methods for pattern recognition and learning, biologically inspired vision, algebraic methods for visual information processing and estimation of 3D information from video sequences. This includes dynamic face modelling.

 

44. University of Maryland, Center for Automation Research

 

The Center for Automation Research (CfAR) at the University of Maryland focuses on research and education involving computer vision, computer visualization, perceptual interfaces, and language and media processing.

There are four laboratories associated with CfAR. The Computer Vision Laboratory includes study into visual biometrics, multi-perspective vision, visual surveillance, image and video database systems, shape representation and object recognition.

 

45. University of Massachusetts, Computer Vision Lab

 

The Computer Vision Lab concentrates its research on the construction of integrated vision systems and the application of vision to problems of real-world importance. One recent paper is "Learning Hierarchical Representations for Face Verification with Convolutional Deep Belief Networks".

 

46. University of Muenster, Computer Vision and Pattern Recognition Group

 

The Computer Vision and Pattern Recognition Group deals with image processing, analysis and understanding, and classification of data.

 

47. University of Notre Dame, Center for Research Computing

 

Examples of research in facial recognition at University of Notre Dame. Among the research topics covered are:

• Recognition of Facial Attributes Using Adaptive Sparse Representations of Random Patches

• Double Trouble: Differentiating Identical Twins by Face Recognition

• The Challenge of Face Recognition from Digital Point-and-Shoot Cameras

• Face Recognition from Video: A Review

 

48. University of Ottawa, Perception and Cognition Lab (PCL)

 

The primary focus of research at the Perception and Cognition Lab is visual perception, especially of faces, although they also do work in object recognition, motion detection and form discrimination. Their cognitive work, also centers on faces, exploring such topics as how faces are memorized and represented in the brain. They study disorders of cognition that arise in such conditions as Fragile X Syndrome, Mild Cognitive Impairment and Autism Spectrum Disorders.

 

49. University of Oulu, Center for Machine Vision Research, Department of Computer Science and Engineering

 

The Center for Machine Vision Research's areas of study range from generic computer vision methodologies to machine vision applications and vision systems engineering.

The main areas of their research are:

• Computer vision methods

• Human-centered vision systems

• Vision systems engineering

The group has a long experience of investigating face-related problems since 1997. Their more recent work focuses on applying manifold learning techniques to face analysis (face recognition, age estimation, gender classification etc.) and on implementing face-related applications in mobile phones.

 

50. The University of Queensland, Australia - Intelligent Real-Time Imaging and Sensing Group, School of Information Technology and Electrical Engineering.

 

The website no longer appears to be online, but there is still a paper available. The paper (Face and Object Recognition and Detection Using Colour Vector Quantisation) presents an approach to face and object detection and recognition based on an extension of the content based image retrieval method of Lu and Teng (1999).

 

51. University of Siegen, Media Systems Research Group, Institute for Vision and Graphics

 

The projects here explore how class-specific information can help to solve problems in image synthesis (Computer Graphics), image analysis (Computer Vision) and 3D shape processing, by learning common properties of object classes from datasets of examples. This includes model-based face recognition.

 

52. University of St Andrews, The Perception Lab, School of Psychology and Neuroscience

 

The Perception Lab investigates the many facets of face perception... what makes one person appear more trustworthy and cooperative than another? What is the relationship between health and attractiveness, and which physiological factors influence this relationship? How big do differences between facial characteristics have to be for us to perceive them?

 

53. University of Victoria (Canada), Rigi Research

 

The focus here is on Context-Based Face Recognition. There is still substantial progress possible for face recognition in less-controlled or uncontrolled conditions, such as in personal photo collections, web images and videos, and online social networks. Searching over a large set of images only amplifies such difficulties. These have sparked research into more robust face recognition methods. This research uses context-based approaches for uncontrolled condition face recognition systems to investigate context-awareness techniques on databases, detection processes, and face recognition processes.

 

54. University of the West of England, BRL Centre for Machine Vision

 

The Centre for Machine Vision aims to solve practical computer vision problems with direct application to the real world. The group’s particular expertise lies in the subfields of three-dimensional reconstruction and surface inspection. One specific project was PhotoFace - face recognition using photometric stereo.

 

55. University of Western Australia research groups, including

 

a. Machine Intelligence Group

This YouTube page demonstrates some of the work of the Machine Intelligence Group. This includes hyperspectral face recognition and 3D facial landmark detection.

b. Complex Data Modelling Group

The Complex Data Modelling Group's research focus is on the challenges of model-building, in the face of engineering data. One area covered is bioengineering, with specific emphasis on:

• computational and systems biology and computational physiology

• biomechanics (computational, soft tissue, prosthesis design and integration with biological systems)

• biomaterials (tissue properties and engineering, bio-replacement materials)

• biosensors and systems

• biomedical optics, biophotonics, and bioimaging

• clinical medicine (medical device engineering, biomedical diagnostics, surgical guidance and simulation, medical imaging and analysis, mathematical medicine, e-health informatics)

 

56. University of Zagreb, Video Communications Laboratory, Department of Wireless Communications

 

Recent research topics are:

• Picture Quality Management in Digital Video Broadcasting

• Intelligent Image Features Extraction in Knowledge Discovery Systems (a scientific project funded by the Ministry of Science, Education and Sports of the Republic of Croatia (MZOS), whose purpose is to create intelligent methods for solving difficult high-level image feature extraction and analysis problems)

• Environment for Satellite Positioning

  The website does not appear to have been updated since 2011.

 

Conclusion

 

The study of facial recognition, facial detection, emotion in faces, and the whole field of facial image processing has been fascinating. There has been huge advancement in a relatively short period of time.

Who knows how far it will go. Where will the budding researchers, now playing and learning in kindergarten, place their focus when they get to their academic prime?