Gesture recognition is an exciting topic in computer science. Research in this field proposes to create a mathematical means of interpreting human gestures, such as facial expressions and hand movements, that would allow computers to recognize and interpret them consistently and correctly. There are a multitude of important applications, and the field in general has many applications for how we may use advanced technology, such as robots and other forms of automation, in the future.
Hand Gesture Recognition
Hand gesture recognition is one obvious way to create a useful, highly adaptive interface between machines and their users. In the ideal scenario, hand gesture recognition technology would allow for the operation of complex machines using only a series of finger and hand movements, eliminating the need for physical contact between operator and machine.. Hand gesture recognition, though already fairly advanced, must still overcome problems related to its use in less hospitable conditions, such as in poor lighting. Some commercial products using gesture recognition are already available, but there is much potential for the technology to improve.
HandVu: Vision-Based Hand Gesture Interface: Archived information about the development and use of a user-friendly software suite intended to track and record hand gestures. Intended for use in controlling applications that support a human gesture interface and for developing data sets for further research in gesture recognition. Full software is available free of charge.
“Hand Gesture Recognition for Human-Machine Interaction”: Scholarly article that originally appeared in the Journal of the Winter School of Computer Graphics, an international journal of research in algorithms, data structures, and computerized visualization. Discusses recent developments in human-machine interactivity on the gesture and body language level.
Journal of W/SCG: Publicly accessible archive of full-text articles from this scholarly journal of mathematical and computer visualization engineering, dating back to 1992 and including several articles pertaining to different subjects in gesture recognition.
Machine Vision Group: Project page from Dublin City University that documents an ongoing research effort to develop gesture recognition technology that recognizes movements from video footage. Includes researcher info and a bibliography of related publications, some of which are available online directly from the Group.
“Vision-Based Hand Gesture Recognition”: Article from the World Academy of Science, Engineering, and Technology discussing future possibilities in natural computer interaction with gesture analysis, and current research trends.
“Real-Time Hand Gesture Recognition Including Hand Segmentation and Tracking”: In-depth description of a system for automated gesture recognition, as written by researchers associated with the 2nd International Symposium on Visual Computing.
Dynamic Hand Gesture Recognition Using the Skeleton of the Hand: Proposal for a system of “dynamic” (movement-oriented) gesture recognition using the skeleton of the hand as a basic reference. Originally from the EURASIP Journal on Applied Signal Processing.
Facial Gesture Recognition
Facial gesture recognition is another way of creating an effective non-contact interface between users and their machines. The goal of facial gesture recognition is for machines to effectively understand emotions and other communication cues within humans, regardless of the countless physical differences between individuals. Like hand gesture recognition, this technology faces its own set of unique problems, caused by physical differences in human faces – not comparable to differences in the hands, which tend to be relatively few – and cultural differences in how facial gestures are used.
“Facial Gesture Recognition in Face Image Sequences”: Article discusses the background and technical details of one approach to the problem of interpreting facial gestures in different contexts; in essence, when the individual is speaking or not speaking. From Imperial College in London.
Facial Recognition Homepage: Website devoted to all aspects of facial recognition research and applications, including links to relevant information on algorithms, research groups from around the world, publicly accessible research papers, and more. Also links to journals and special publications focused on facial recognition.
Machine Analysis of Facial Expressions: Heavily cited article on the basics of facial recognition and the challenges associated with it. Joint work between Imperial College of London and the University of California.
Facial Gesture Interfaces for Expression and Communication: Scholarly overview of recent research projects that use a vision-oriented approach to interpreting facial gestures for human-computer interaction.
The Role of Context in Head Gesture Recognition: Discussion of a recognition framework aimed at improving facial gesture recognition by analysis of contextual clues and enhanced prediction of basic gestures such as head nods and head shakes.
Eye Gesture Recognition: Focuses on the use of operators’ eye gestures as a means of controlling computers. Includes detailed information on current research methods and future applications.
Uses of Gesture Recognition
In general, gesture recognition has many potential uses. Hand gesture recognition can be used in scientific research, construction, and any situation where user and machine must be separated, such as dangerous areas suited to mechanical exploration. Gesture recognition can be used in creating adaptable assistive technologies for persons with various physical and sensory handicaps. There are several communicative applications, such as sign language recognition. Pure facial gesture recognition can be used to great effect for purposes related to personal security and law enforcement.
Gesture Recognition Review: Overview of quality Internet resources, published studies, and books pertaining to all forms of gesture recognition. Pieces cover current research and applications as well as projections of future directions in the field.
“Television Control by Hand Gestures”: Report by Mitsubishi Electric Research Laboratories on a method of remotely controlling a television using only hand gestures.
“Gesture Recognition in Virtual Reality Environments”: Discusses the challenges and current frontiers of gesture recognition for fully immersive entertainment environments. Part of the Encyclopedia of Virtual Environments produced as part of a course at Washington University.
“Gesture Recognition in Acoustic-Based Touch Interfaces”: Detailed, illustrated step-by-step analysis of a system for controlling machines through finger and hand gestures that produce specific sounds against a surface.
“Extending an Existing User Interface Toolkit to Support Gesture Recognition”: From the College of Computer Science at Carnegie Mellon University, a brief introduction to the challenges of expanding current user interfaces with gesture recognition.
“A Wearable Camera System for Gesture Recognition”: Research pertaining to interpreting pointing-type gestures using a novel application of camera technology. From Washington University in St. Louis.
Sign Language Recognition
Sign language recognition is one of the most promising sub-fields in gesture recognition research. Effective sign language recognition would grant the deaf and hard-of-hearing expanded tools for communicating with both other people and machines. In order to realize this technology, researchers must devise methods to capture and record both individual hand positions and the motions that create them for a constant, fluid, and accurate interpretation of sign language. Because of its potential therapeutic and social justice benefits, sign language recognition is one of the most pursued subfields in gesture recognition research.
“Automated Sign Language Recognition: Past, Present, and Future”: Notes from a talk given at Gallaudet University about the future of sign language recognition technology. Includes several movies that serve as companion pieces to the text.
Mobile Sign Language Recognition Research Group: Research group from Georgia Technical College that includes several published research papers as well as news, profiles, and other resources.
“Real-Time American Sign Language Recognition From Videos”: Describes a system of sign language recognition using Hidden Markov Models, a particular kind of dynamic statistical model.
Research: Sign Language Recognition: Information and a huge list of research publications pertaining to sign language recognition, compiled in association with the Department of Computer Science at RWTH Aachen University in Germany.
“Real-Time ASL Recognition Using Desk and Wearable Computer-Based Video”: Research from MIT’s Media Library detailing Hidden Markov Models for sign language interpretation to obtain a 95%-97% accuracy rate.
“Automation of Arabic Sign Language Recognition Using PowerGlove”: Research on Arabic sign language computation using a specialized electronic glove for direct interaction without a separate input interface.
“Sign Language Recognition and Translation”: Interdisciplinary overview of the various approaches to sign language recognition and the possible applications. Written by a researcher at the University of North Texas; serves as an accessible overview and definition of the key terms and modes of research.
Ongoing research continues to seek out new ways of synthesizing natural human gestures and computerized interpretive structures. From basic entertainment applications, such as interactive gameplay and remote operation of an ever-wider array of electronics, to the frontiers of computer and robotic operations using voice commands, new modes of interfacing with machines are appearing more and more regularly thanks to gesture recognition research. Soon, it may be possible to break down the basic barriers that make secondary input devices such as keyboards necessary. Over time, all manner of machines may come to be operated through natural gestures, making human interaction with their tools and world more dynamic and fluid than ever before in modern times.
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