Image Object Localization by AdaBoost Classifier
AdaBoost as a methodology of aggregation of many weak classifiers into one strong classifier is used now in object detection in images. In particular it appears very efficient in face detection and eye localization. In order to improve the speed of the classifier we show a new scheme for the decision cost evaluation. The aggregation scheme reduces the number of weak classifiers and provides better performance in terms of false acceptance and false rejection ratios.
KeywordsFace Detection Weak Classifier Exponential Convergence False Acceptance Rate False Rejection Rate
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