The Robotics Institute

Carnegie Mellon Robotics Institute

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract

Free-parts representations of the face, based on parametric distributions such as Gaussian mixture models (GMMs), have recently demonstrated benefit in the task of face verification. This benefit can be largely attributed to the representation's natural ability to deal with local appearance variation within the face. Hitherto, a major limitation that has hindered the wider adoption of this type of facial representation, for the task of face verification, has been its poor ability to take advantage of prior knowledge concerning mismatches in context; such as pose (e.g. gallery face=frontal pose, probe face=non-frontal pose). This paper goes some way to alleviating these limitations by making two novel contributions: (i) Demonstrating, via a novel theoretical framework, that a lower-bound can be empirically calculated for how much discriminating information exists for a pre-defined pose mismatch; assuming there is no conditional dependence between observations stemming from different poses. (ii) Through the off-line estimation of subject-independent pose dependent priors, a number of alternatives to the canonical log-likelihood can be employed that enjoy improved performance in the presence of pose mismatch.

Keywords

Face Recognition, Pose Mismatch

Notes

Associated Center(s) / Consortia: Vision and Autonomous Systems Center

Associated Lab(s) / Group(s): Human Identification at a Distance, Face Group, Component Analysis

Associated Project(s): Facial Expression Analysis and Face Recognition Across Pose

Number of pages: 10

Text Reference

Simon Lucey, "Using overlapping distributions to deal with face pose mismatch," British Machine Vision Conference (BMVC), September, 2005.

BibTeX Reference

@inproceedings{Lucey_2005_5493,
   author = "Simon Lucey",
   title = "Using overlapping distributions to deal with face pose mismatch",
   booktitle = "British Machine Vision Conference (BMVC)",
   month = "September",
   year = "2005",
}

The Robotics Institute

Menu