An Evaluation of Local Features for Face Detection and Localization

Local features have the ability to overcome the major drawback of traditional, holistic object detection approaches, because they are inherently invariant to geometric deformation and pose; in addition scale and rotation invariance can be easily achieved as well. However, the selection of discriminative feature locations and local descriptions is a complex task that has not been generally solved. In case of face detection, features must possess the discriminative power to differentiate between facial parts and cluttered backgrounds while they have to remain person agnostic. A multitude of suggestions for selecting facial features for tracking or identification / recognition can be found in literature, most of which rely on semi-automatic or manual definition of the feature locations. In contrast, fully automatic feature selection and generic description approaches like SIFT and SURF have been shown to provide excellent performance for rigid as well as non-rigid registration and even for object class recognition. While quantitative evaluations exist that give a hint on the registration performance of the competing designs, these scenarios are not directly transferable to object detection. In this paper we provide qualitative and quantitative analysis of existing interest point detectors as well as local descriptions in the context of face detection and localization.