Abstract for yow_fg96_1

In Proceedings 2nd International Conference on Automatic Face and Gesture Recognition, Vermont, USA


Kin Choong Yow and Roberto Cipolla

October 1996

Present approaches to human face detection have made several assumptions that restrict their ability to be extended to general imaging conditions. We identify that the key factor in a generic and robust system is that of exploiting a large amount of evidence, related and reinforced by model knowledge through a probabilistic framework. In this paper, we propose a face detection framework that groups image features into meaningful entities using perceptual organization, assigns probabilities to each of them, and reinforce these probabilities using Bayesian reasoning techniques. True hypotheses of faces will be reinforced to a high probability. The detection of faces under scale, orientation and viewpoint variations will be examined in a subsequent paper.

(ftp:) yow_fg96_1.ps.Z (http:) yow_fg96_1.ps.Z
PDF (automatically generated from original PostScript document - may be badly aliased on screen):
  (ftp:) yow_fg96_1.pdf | (http:) yow_fg96_1.pdf

If you have difficulty viewing files that end '.gz', which are gzip compressed, then you may be able to find tools to uncompress them at the gzip web site.

If you have difficulty viewing files that are in PostScript, (ending '.ps' or '.ps.gz'), then you may be able to find tools to view them at the gsview web site.

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.