Fast and robust face finding via local context

Abstract

In visual surveillance face detection can be an important cue for initializing tracking algorithms. Recent work in psychophics hints at the importance of the local context of a face for robust detection, such as head contours and torso. This paper describes a detector that actively utilizes the idea of local context. The promise is to gain robustness that goes beyond the capabilities of traditional face detection making it particularly interesting for surveillance. The performance of the proposed detector in terms of accuracy and speed is evaluated on data sets from PETS 2000 and PETS 2003 and compared to the object-centered approach. Particular attention is paid to the role of available image resolution.