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Face Detection and Gesture Recognition for Human-Computer Interaction

Traditionally, scientific fields have defined boundaries, and scientists work on research problems within those boundaries. However, from time to time those boundaries get shifted or blurred to evolve new fields. For instance, the original goal of computer vision was to understand a single image of...

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Bibliographic Details
Main Authors: Ming-Hsuan Yang, Ahuja, Narendra (Author)
Format: eBook
Language:English
Published: New York, NY Springer US 2001, 2001
Edition:1st ed. 2001
Series:The International Series in Video Computing
Subjects:
User Interfaces (computer Systems)
Image Processing / Digital Techniques
Computer Vision
Artificial Intelligence
Computer Imaging, Vision, Pattern Recognition And Graphics
Mechanical Engineering
User Interfaces And Human Computer Interaction
Human-computer Interaction
Online Access:
Collection: Springer Book Archives -2004 - Collection details see MPG.ReNa
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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