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Face detection is widely used in interactive user interfaces and plays a very important role in the field of computer vision. In order to build a fully automated system that can analyze the information in face image, there is a need for robust and efficient face detection algorithms. One of the fastest and most successful approaches in this field is to use Haar-like features for facial appearance...
Aiming at unloading the high training time burden of the popular cascaded classifier, in this paper, a novel cascade structure called Fea-Accu cascade is proposed. In Fea-Accu cascade training, the times of feature selection are largely reduced by enhancing the correlation among different stage classifiers of the cascaded classifier. In detail, for each stage classifier, before selecting new features...
In this paper, we briefly review AdaBoost and expand on the discrete version by building weak classifiers from a pair of biased classifiers which enable the weak classifier to abstain from classifying some samples. We show that this approach turns into a 3-bin real AdaBoost approach where the bin sizes and positions are set by the bias parameters selected by the user and dynamically change with every...
This paper presents a new variant of AdaBoost based on Viola and Jonespsila framework, called Z-AdaBoost. Instead of modifying the mechanism of selecting optimal weak classifiers as other variants do, Z-AdaBoost strengthens the discriminating power of weak classifiers themselves by expanding them into 2-thresholded ones, which guarantees a better classification with smaller error. And a linear online...
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