An Alternative Scanning Strategy to Detect Faces

The sliding window approach is the most widely used technique to detect faces in an image. Usually a classifier is applied on a regular grid and to speed up the scanning, the grid spacing is increased, which increases the number of miss detections. In this paper we propose an alternative scanning method which minimizes the number of misses, while improving the speed of detection. To achieve this we use an additional classifier that predicts the bounding box of a face within a local search area. Then a face/non-face classifier is used to verify the presence or absence of a face. We propose a new combination of binary features which we term as u-Ferns for bounding box estimation, which performs comparable or better than former techniques. Experimental evaluation on benchmark database show that we can achieve 15-30% improvement in detection rate or speed when compared to the standard scanning technique.