SCALE AND ORIENTATION INVARIANCE IN HUMAN FACE DETECTION
Kin Choong Yow and Roberto Cipolla
Many current human face detection algorithms make implicit assumptions about the scale, orientation or viewpoint of faces in an image and exploit these constraints to detect and localize faces. The algorithm may be robust for the assumed conditions but however it becomes very difficult to extend the results to general imaging conditions. In an earlier paper, we proposed a feature-based face detection algorithm to detect faces in complex background. In this paper, we will examine its ability to detect faces under different scale, orientation and viewpoint. The results show that the algorithm can indeed cope with a good range of scale, orientation and viewpoint variations that is typical of a subject sitting in front of a computer terminal.
If you have difficulty viewing files that end
which are gzip compressed, then you may be able to find
tools to uncompress them at the gzip
If you have difficulty viewing files that are in PostScript, (ending
'.ps.gz'), then you may be able to
find tools to view them at
We have attempted to provide automatically generated PDF copies of documents for which only PostScript versions have previously been available. These are clearly marked in the database - due to the nature of the automatic conversion process, they are likely to be badly aliased when viewed at default resolution on screen by acroread.