A GMM parts based face representation for improved verification through relevance adaptation

  • Authors:

  • Simon Lucey;Tsuhan Chen

  • Affiliations:

  • Advanced Multimedia Processing Laboratory, Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA;Advanced Multimedia Processing Laboratory, Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA

  • Venue:
  • CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
  • Year:
  • 2004

Quantified Score

Hi-index 0.04

Visualization

Abstract

Motivated by the success of parts based representations in face detection we have attempted to address some of the problems associated with applying such a philosophy to the task of face verification. Hitherto, a major problem with this approach in face verification is the intrinsic lack of training observations, stemming from individual subjects, in order to estimate the required conditional distributions. The estimated distributions have to be generalized enough to encompass the differing permutations of a subject's face yet still be able to discriminate between subjects. In our work the well known Gaussian mixture model (GMM) framework is employed to model the conditional density function of the parts based representation of the face. We demonstrate that excellent performance can be obtained from our GMM based representation through the employment of adaptation theory, specifically relevance adaptation (RA). Our results are presented for the frontal images of the BANCA database.