Feature type and size selection for adaboost face detection algorithm

Jerzy Dembski

Feature type and size selection for adaboost face detection algorithm

Abstract

The article presents different sets of Haar-like features defined for adaptive boosting (AdaBoost) algorithm for face detection. Apart from a simple set of pixel intensity differences between horizontally or vertically neighboring rectangles, the features based on rotated rectangles are considered. Additional parameter that limits the area on which the features are calculated is also introduced. The experiments carried out on the set of MIT 19x19 face and non-face examples showed the usefulness of particular types of features and their influence on generalization.

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Jerzy Dembski dr inż.

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Category:: Monographic publication
Type:: rozdział, artykuł w książce - dziele zbiorowym /podręczniku w języku o zasięgu międzynarodowym
Title of issue:: Image Processing & Communications/ Image Processing and Communication Challenges 2 strony 143 - 149
Language:: English
Publication year:: 2010
Bibliographic description:: Dembski J.: Feature type and size selection for adaboost face detection algorithm// Image Processing & Communications/ Image Processing and Communication Challenges 2/ ed. ed. Ryszard S. Choraś, Uniwersytet Technologiczno-Przyrodniczy w Bydgoszczy. Chennai, Indie: Springer-Verlag Berlin Heidelberg, 2010, s.143-149
Verified by:: Gdańsk University of Technology