  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.
