Overview

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.

Full Product Details

Author:   Ming-Hsuan Yang ,  Narendra Ahuja
Publisher:   Springer
Imprint:   Springer
Volume:   1
Dimensions:   Width: 15.50cm , Height: 1.20cm , Length: 23.50cm
Weight:   1.020kg
ISBN:  

9780792374091

ISBN 10:   0792374096
Pages:   182
Publication Date:   31 August 2001
Audience:   College/higher education ,  Professional and scholarly ,  Undergraduate ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

1. Introduction.- 1. Face Detection.- 2. Gesture Recognition.- 3. Book Overview.- 2. Detecting Faces in Still Images.- 1. Introduction.- 2. Detecting Faces In A Single Image.- 3. Face Image Databases and Performance Evaluation.- 4. Discussion and Conclusion.- 3. Recognizing Hand Gestures Using Motion Trajectories.- 1. Introduction.- 2. Motivation and Approach.- 3. Motion Segmentation.- 4. Skin Color Model.- 5. Geometric Analysis.- 6. Motion Trajectories.- 7. Recognizing Motion Patterns Using Time-Delay Neural Network.- 8. Experiments.- 9. Discussion and Conclusion.- 4. Skin Color Model.- 1. Proposed Mixture Model.- 2. Statistical Tests.- 3. Experimental Results.- 4. Applications.- 5. Discussion and Conclusion.- 5. Face Detection Using Multimodal Density Models.- 1. Introduction.- 2. Previous Work.- 3. Mixture of Factor Analyzers.- 4. Mixture of Linear Spaces Using Fisher’s Linear Discriminant.- 5. Experiments.- 6. Discussion and Conclusion.- 6. Learning to Detect Faces with SNoW.- 1. Introduction.- 2. Previous Work.- 3. SNoW Learning Architecture.- 4. Learning to Detect Faces.- 5. Empirical Results.- 6. Analyzing SNoW: Theoretical and Empirical Results.- 7. Generation and Efficiency.- 8. Discussion and Conclusion.- 7. Conclusion and Future Work.- 1. Conclusion.- 2. Future Work.- Appendices.- A– Covariance of Two Normally Distributed Variables.- B– Conditional Distributions of Multiple Correlation Coefficient.- References.

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