Accelerating face detection by using depth information

Abstract

In the case that the sizes of faces are not available, all possible sizes of faces have to be assumed and a face detector has to classify many (often ten or more) sub-image regions everywhere in an image. This makes the face detection slow and the high false positive rate. This paper explores the usage of depth information for accelerating the face detection and reducing the false positive rate at the same time. In detail, we use the depth information to determine the size of the sub-image region that needs to be classified for each pixel. This will reduce the number of sub-image regions that need to be classified from many to one for one position (pixel) in an image. Since most unnecessary classifications are effectively avoided, both the processing time for face detection and the possibility of false positive can be reduced greatly. We also propose a fast algorithm for estimating the depth information that is used to determine the size of sub-image regions to be classified. © 2009 Springer Berlin Heidelberg.