Examplers based image fusion features for face recognition
Abstract
Examplers of a face are formed from multiple gallery images of a person and are used in the process of classification of a test image. We incorporate such examplers in forming a biologically inspired local binary decisions on similarity based face recognition method. As opposed to single model approaches such as face averages the exampler based approach results in higher recognition accu- racies and stability. Using multiple training samples per person, the method shows the following recognition accuracies: 99.0% on AR, 99.5% on FERET, 99.5% on ORL, 99.3% on EYALE, 100.0% on YALE and 100.0% on CALTECH face databases. In addition to face recognition, the method also detects the natural variability in the face images which can find application in automatic tagging of face images.
Cite
@article{arxiv.1201.5947,
title = {Examplers based image fusion features for face recognition},
author = {Alex Pappachen James and Sima Dimitrijev},
journal= {arXiv preprint arXiv:1201.5947},
year = {2012}
}