Analysis of partial least squares for pose-invariant face recognition

Face recognition across large pose changes is one of the hardest problems for automatic face recognition. Recently, approaches that use partial least squares (PLS) to compute pairwise pose-independent coupled subspaces have achieved good results on this problem. In this paper, we perform a thorough experimental analysis of the PLS approach for pose-invariant face recognition. We find that the use of different alignment methods can have a significant influence on the results. We propose a simple and consistent alignment method that is easily reproducible and uses only few hand-tuned parameters. Further, we find that block-based approaches outperform those using a holistic face representation. However, we note that the size, positioning and selection of the extracted blocks has a large influence on the performance of PLS-based approaches, with the optimal sizes and selections differing significantly for different feature representations. Finally, we show that local PLS using simple intensity values performs almost as well as more sophisticated feature extraction methods like Gabor features for frontal gallery images. However, Gabor features perform significantly better with non-frontal gallery images. The achieved results exceed the previously reported results for the CMU Multi-PIE dataset on this task with an average recognition rate of 90.1% when using frontal images as gallery and 82.0% when considering all pose pairs.