Skip to content

Advertisement

  • Research Article
  • Open Access

Face Recognition Incorporating Ancillary Information

EURASIP Journal on Advances in Signal Processing20072008:312849

https://doi.org/10.1155/2008/312849

  • Received: 1 May 2007
  • Accepted: 16 September 2007
  • Published:

Abstract

Due to vast variations of extrinsic and intrinsic imaging conditions, face recognition remained to be a challenging computer vision problem even today. This is particularly true when the passive imaging approach is considered for robust applications. To advance existing recognition systems for face, numerous techniques and methods have been proposed to overcome the almost inevitable performance degradation due to external factors such as pose, expression, occlusion, and illumination. In particular, the recent part-based method has provided noticeable room for verification performance improvement based on the localized features which have good tolerance to variation of external conditions. The part-based method, however, does not really stretch the performance without incorporation of global information from the holistic method. In view of the need to fuse the local information and the global information in an adaptive manner for reliable recognition, in this paper we investigate whether such external factors can be explicitly estimated and be used to boost the verification performance during fusion of the holistic and part-based methods. Our empirical evaluations show noticeable performance improvement adopting the proposed method.

Keywords

  • Performance Improvement
  • Face Recognition
  • Adaptive Manner
  • Empirical Evaluation
  • Global Information

Publisher note

To access the full article, please see PDF.

Authors’ Affiliations

(1)
School of Electrical and Electronic Engineering, Yonsei University, Seoul, 120-749, South Korea

Copyright

© Sang-Ki Kim et al. 2008

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement