Using sparse regression to learn effective projections for face recognition

Authors:
Yongxin Taylor Xi;Peter J. Ramadge
Affiliations:
Dept. Electrical Engineering, Princeton University, Princeton, NJ;Dept. Electrical Engineering, Princeton University, Princeton, NJ
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

Citing 6
Cited 0

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition: A literature survey

ACM Computing Surveys (CSUR)
Regularization studies of linear discriminant analysis in small sample size scenarios with application to face recognition

Pattern Recognition Letters
Eigenfaces for recognition

Journal of Cognitive Neuroscience
Individual discriminative face recognition models based on subsets of features

SCIA'07 Proceedings of the 15th Scandinavian conference on Image analysis

Quantified Score

Hi-index	0.00

Visualization

Abstract

We explore sparse regression for effective feature selection and classification in face identity and expression recognition. We argue that sparse regression in pixel space is inappropriate. We propose instead a method which combines the virtues of sparse regression with projection methods such as PCA and FDA. The method can learn a sparse set of discriminative projections and increase recognition accuracy beyond that achievable by FDA.We demonstrate this by performance comparisons on three face data sets.