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CVPR
2004
IEEE

Automatic Cascade Training with Perturbation Bias

10 years 4 days ago
Automatic Cascade Training with Perturbation Bias
Face detection methods based on a cascade architecture have demonstrated fast and robust performance. Cascade learning is aided by the modularity of the architecture in which nodes are chained together to form a cascade. In this paper we present two new cascade learning results which address the decoupled nature of the cascade learning task. First, we introduce a cascade indifference curve framework which connects the learning objectives for a node to the overall cascade performance. We derive a new cost function for node learning which yields fully-automatic stopping conditions and improved detection performance. Second, we introduce the concept of perturbation bias which leverages the statistical differences between target and nontarget classes in a detection problem to obtain improved performance and robustness. We derive necessary and sufficient conditions for the success of the method and present experimental results.
Jie Sun, James M. Rehg, Aaron F. Bobick
Added 12 Oct 2009
Updated 29 Oct 2009
Type Conference
Year 2004
Where CVPR
Authors Jie Sun, James M. Rehg, Aaron F. Bobick
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