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E-raamat: Face Detection and Gesture Recognition for Human-Computer Interaction

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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.

Muu info

Springer Book Archives
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 Fishers
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.