Using a generalized linear mixed model to study the configuration space of a PCA+LDA human face recognition algorithm

A generalized linear mixed model is used to estimate how rank 1 recognition of human faces with a PCA+LDA algorithm is affected by the choice of distance metric, image size, PCA space dimensionality, supplemental training, and the inclusion of test subjects in the training data. Random effects for replicated training sets and for repeated measures on people were included in the model. Results indicate that variation among subjects was a dominant source of variability, and that there was moderate correlation within people. Statistically significant effects and interactions were found for all configuration factors except image size. The most significant interaction is that dhanges to the PCA+LDA configuration only improved recognition for test subjects who were included in the training data. For subjects not included in training, no configuration changes were helpful. This study is a model for how to evaluate algorithms with large numbers of parameters. For example, by accounting for subject variation as a random effect and explicitly looking for interaction effects, we are able to discern effects that might otherwise have been masked by subject variation and interaction effects. This study is instructive for what it reveals about PCA+LDA.