Face Detection Based on Fuzzy Cascade Classifier with Scale-invariant Features

Viola et al. have introduced a rapid object detection framework based on a boosted cascade of simple feature classifiers. In this paper we extend their work and achieve two contributions. Firstly, we propose a novel feature definition and introduce a feature shape mask to represent it. The defined features are scale-invariant which means the features can be rescaled easily and reduce the performance degradation introduced by rounding. The feature shape mask can be computed efficiently and expanded conveniently, which can simulate feature shapes used by others and thus enriches the haar-like feature pool. Secondly, we present an improved cascade-structured classifier which is called fuzzy cascade classifier. The cascade-structured classifier owns the disadvantage of neglecting confidence of the prior stage classifiers while only using the binary output of prior stages. Motivated by fuzzy theory, we expand the output of each stage to three states: face, non-face, and potential face and set probability being face to each candidate window to make full use of the information of prior stages. Merged by voting, we improve the hit rate at similar false alarm rate. Keyword: Face detection, AdaBoost, Scale-invariant feature, Fuzzy cascade classifier