Linear Asymmetric Classifier for cascade detectors

The detection of faces in images is fundamentally a rare event detection problem. Cascade classifiers provide an efficient computational solution, by leveraging the asymmetry in the distribution of faces vs. non-faces. Training a cascade classifier in turn requires a solution for the following subproblems: Design a classifier for each node in the cascade with very high detection rate but only moderate false positive rate. While there are a few strategies in the literature for indirectly addressing this asymmetric node learning goal, none of them are based on a satisfactory theoretical framework. We present a mathematical characterization of the node-learning problem and describe an effective closed form approximation to the optimal solution, which we call the Linear Asymmetric Classifier (LAC). We first use AdaBoost or AsymBoost to select features, and use LAC to learn a linear discriminant function to achieve the node learning goal. Experimental results on face detection show that LA...

• Type: Conference
• Year: 2005
• Where: ICML
• Authors: Jianxin Wu, Matthew D. Mullin, James M. Rehg
• Keywords: Cascade Classifier, ICML 2005, Linear Asymmetric Classifier, Machine Learning, Node Learning Goal