2D Face Recognition in the IV2 Evaluation Campaign

In this paper, the first evaluation campaign on 2D-face images using the multimodal IV2 database is presented. The five appearance-based algorithms in competition are evaluated on four experimental protocols, including experiments with challenging illumination and pose variabilities. The results confirm the advantages of the Linear Discriminant Analysis (LDA) and the importance of the training set for the Principal Component Analysis (PCA) based approaches. The experiments show the robustness of the Gabor based approach combined with LDA, in order to cope with challenging face recognition conditions. This evaluation shows the interest and the richness of the IV2 multimodal database.