Boosted cascade of scattered rectangle features for object detection

Abstract

This paper presents a variant of Haar-like feature used in Viola and Jones detection framework, called scattered rectangle feature, based on the common-component analysis of local region feature. Three common components, feature filter, feature structure and feature form, are extracted without concerning the details of the studied region features, which cast a new light on region feature design for specific applications and requirements: modifying some component(s) of a feature for an improved one or combining different components of existing features for a new favorable one. Scattered rectangle feature follows the former way, extending the feature structure component of Haar-like feature out of the restriction of the geometry adjacency rule, which results in a richer representation that explores much more orientations other than horizontal, vertical and diagonal, as well as misaligned, detached and non-rectangle shape information that is unreachable to Haar-like feature. The training result of the two face detectors in the experiments illustrates the benefits of scattered rectangle feature empirically; the comparison of the ROC curves under a rigid and objective detection criterion on MIT+CMU upright face test set shows that the cascade based on scattered rectangle features outperforms that based on Haar-like features.