Gaussian weak classifiers based on co-occurring Haar-like features for face detection Articles uri icon

publication date

  • May 2014

start page

  • 431

end page

  • 439

issue

  • 2

volume

  • 17

international standard serial number (ISSN)

  • 1433-7541

electronic international standard serial number (EISSN)

  • 1433-755X

abstract

  • Recently, in the context of appearance-based face detection, it has been shown by Mita et al. that weak classifiers based on co-occurring, or multiple, Haar-like features provide better speed-accuracy trade-off than the widely used Viola and Jones's weak classifiers, which use only a single Haar-like feature. In this paper, we extend Mita et al.'s work by proposing Gaussian weak classifiers that fuse information obtained from the co-occurring features at the feature level, and are potentially more discriminative. Experimental results, on the standard MIT+CMU test images, show that the face detectors built using Gaussian weak classifiers achieve up to 38 % more accuracy in terms of false positives and 42 % decrease in testing time when compared to the detectors built using Mita et al.'s weak classifiers.

keywords

  • haar-like features; object detection; weak classifiers; cascade classifiers; object detection; selection