Face Detection And Gesture Recognition For Human-Computer Interaction

With the ubiquity of new information technology and media, more effective and friendly methods for human computer interaction (HCI) are being developed which do not rely on traditional devices such as keyboards, mice and displays. The first step for any intelligent HCI system is face detection, and one of most friendly HCI systems is hand gesture.
Face Detection and Gesture Recognition for Human-Computer Interaction introduces the frontiers of vision-based interfaces for intelligent human computer interaction with focus on two main issues: face detection and gesture recognition. The first part of the book reviews and discusses existing face detection methods, followed by a discussion on future research. Performance evaluation issues on the face detection methods are also addressed. The second part discusses an interesting hand gesture recognition method based on a generic motion segmentation algorithm. The system has been tested with gestures from American Sign Language with promising results. We conclude this book with comments on future work in face detection and hand gesture recognition.
Face Detection and Gesture Recognition for Human-Computer Interaction will interest those working in vision-based interfaces for intelligent human computer interaction. It also contains a comprehensive survey on existing face detection methods, which will serve as the entry point for new researchers embarking on such topics. Furthermore, this book also covers in-depth discussion on motion segmentation algorithms and applications, which will benefit more seasoned graduate students or researchers interested in motion pattern recognition.

With the ubiquity of new information technology and media, more effective and friendly methods for human computer interaction (HCI) are being developed which do not rely on traditional devices such as keyboards, mice and displays. The first step for any intelligent HCI system is face detection, and one of most friendly HCI systems is hand gesture.
Face Detection and Gesture Recognition for Human-Computer Interaction introduces the frontiers of vision-based interfaces for intelligent human computer interaction with focus on two main issues: face detection and gesture recognition. The first part of the book reviews and discusses existing face detection methods, followed by a discussion on future research. Performance evaluation issues on the face detection methods are also addressed. The second part discusses an interesting hand gesture recognition method based on a generic motion segmentation algorithm. The system has been tested with gestures from American Sign Language with promising results. We conclude this book with comments on future work in face detection and hand gesture recognition.
Face Detection and Gesture Recognition for Human-Computer Interaction will interest those working in vision-based interfaces for intelligent human computer interaction. It also contains a comprehensive survey on existing face detection methods, which will serve as the entry point for new researchers embarking on such topics. Furthermore, this book also covers in-depth discussion on motion segmentation algorithms and applications, which will benefit more seasoned graduate students or researchers interested in motion pattern recognition.

Booknews

Researchers have been working under the premise that a computer can recognize information about a user's identity, state, and intent from facial expression and hand motions. Because of the immense number of variables in processing facial images (scale, location, orientations, pose, etc) the first step is getting computers to recognize faces. Gesture recognition is similarly complicated. Yang (Honda R&D Americas, Inc.) and Ahuja (computer science, U. of Illinois at Urbana-Champaign) present their own work in these related fields and summarize some recent findings by other researchers. They present an algorithm for extracting and recognizing motion patterns, present experimental results on motion patterns related to 40 American Sign Language gestures, discuss a model that extracts skin tone regions for the reduction of computation needed, present experimental results for the model, and describe a (Sparse Network of Winnows) SnoW-based face detector. Finally future directions of research are examined. Annotation c. Book News, Inc., Portland, OR (booknews.com)