An Independent Evaluation of Subspace Face Recognition Algorithms
Abstract
This paper explores a comparative study of both the linear and kernel implementations of three of the most popular Appearance-based Face Recognition projection classes, these being the methodologies of Principal Component Analysis, Linear Discriminant Analysis and Independent Component Analysis. The experimental procedure provides a platform of equal working conditions and examines the ten algorithms in the categories of expression, illumination, occlusion and temporal delay. The results are then evaluated based on a sequential combination of assessment tools that facilitate both intuitive and statistical decisiveness among the intra and interclass comparisons. The best categorical algorithms are then incorporated into a hybrid methodology, where the advantageous effects of fusion strategies are considered.
Cite
@article{arxiv.0705.0952,
title = {An Independent Evaluation of Subspace Face Recognition Algorithms},
author = {Dhiresh R. Surajpal and Tshilidzi Marwala},
journal= {arXiv preprint arXiv:0705.0952},
year = {2007}
}