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.

• Type: Conference
• Year: 2004
• Where: CVPR
• Authors: Jie Sun, James M. Rehg, Aaron F. Bobick
• Keywords: Cascade Architecture, Cascade Indifference Curve, Cascade Learning Task, Computer Vision, CVPR 2004, Fully-automatic Stopping Conditions, Overall Cascade Performance