RI | Publications | Representational Oriented Component Analysis (ROCA) for Face Recognition with One Sample Image per Training Class

Text only version of this site

Representational Oriented Component Analysis (ROCA) for Face Recognition with One Sample Image per Training Class
F. De la Torre Frade, R. Gross, S. Baker, and V. Kumar
Computer Vision and Pattern Recognition, Vol. 2, June, 2005, pp. 266 - 273.

Jump to: Download | Abstract | Notes | Text Reference | BibTeX Reference

Download [Help]

Adobe portable document format (pdf) [559 KB]

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

Subspace methods such as PCA, LDA, ICA have become a standard tool to perform visual learning and recognition. In this paper we propose Representational Oriented Component Analysis (ROCA), an extension of OCA, to perform face recognition when just one sample per training class is available. Several novelties are introduced in order to improve generalization and efficiency:

1) Combining several OCA classifiers based on different image representations of the unique training sample is shown to greatly improve the recognition performance. 2) To improve generalization and to account for small misregistration effect, a learned subspace is added to constrain the OCA solution. 3) A stable/efficient generalized eigenvector algorithm that solves the small size sample problem and avoids overfitting.

Preliminary experiments in the FRGC Ver 1.0 dataset (http://www.bee-biometrics.org/) show that ROCA outperforms existing linear techniques (PCA,OCA) and some commercial systems.

Notes

Sponsor: DARPA

Associated center: VASC
Associated lab/group: Face Group
Associated projects: Face Recognition and Component Analysis for Data Analysis

Number of pages: 8

Text Reference

F. De la Torre Frade, R. Gross, S. Baker, and V. Kumar, "Representational Oriented Component Analysis (ROCA) for Face Recognition with One Sample Image per Training Class," Computer Vision and Pattern Recognition, Vol. 2, June, 2005, pp. 266 - 273.

BibTeX Reference

@inproceedings{De la Torre Frade_2005_5008,
   author = "Fernando De la Torre Frade and Ralph Gross and Simon Baker and Vijaya Kumar",
   title = "Representational Oriented Component Analysis (ROCA) for Face Recognition with One Sample Image per Training Class",
   booktitle = "Computer Vision and Pattern Recognition",
   month = "June",
   year = "2005",
   volume = "2",
   pages = "266 - 273"
}