Fusion levels of visible and infrared modalities for face recognition

We present a study on different levels of visible and infrared modalities fusion for face recognition. While visible modality is the most natural way to recognize someone, infrared presents thermal distribution that can be useful for face recognition. We compare the well-known eigenfaces method as a baseline to an approach based on sparsity for the feature extraction and the classification. Applied on the Notre-Dame database, we showed that the three levels of fusion considered are not equivalent in term of final identification rates. We also show that the sparse approach at the decision level outperforms the state-of-art on this database.