Today I read a paper titled “An Independent Evaluation of Subspace Face Recognition Algorithms”
The abstract is:
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.