A brief survey of the face detection literature -- Cascade-based real-time face detection -- Multiple instance learning for face detection -- Detector adaptation -- Other applications -- Conclusions and future work
Summary
Face detection, because of its vast array of applications, is one of the most active research areas in computer vision. In this book, we review various approaches to face detection developed in the past decade, with more emphasis on boosting-based learning algorithms. We then present a series of algorithms that are empowered by the statistical view of boosting and the concept of multiple instance learning
Analysis
face detection
boosting
multiple instance learning
adaptation
multiple task learning
multimodal fusion
Bibliography
Includes bibliographical references (pages 113-126)
