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• W4205658999 abstract "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. We start by describing a boosting learning framework that is capable to handle billions of training examples. It differs from traditional bootstrapping schemes in that no intermediate thresholds need to be set during training, yet the total number of negative examples used for feature selection remains constant and focused (on the poor performing ones). A multiple instance pruning scheme is then adopted to set the intermediate thresholds after boosting learning. This algorithm generates detectors that are both fast and accurate." @default.
• W4205658999 created "2022-01-25" @default.
• W4205658999 creator A5038013079 @default.
• W4205658999 creator A5058849717 @default.
• W4205658999 date "2010-12-20" @default.
• W4205658999 modified "2023-10-16" @default.
• W4205658999 title "Boosting-Based Face Detection and Adaptation" @default.
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