Marc Oliu author Ciprian Corneanu author Kamal Nasrollahi author Olegs Nikisins author Sergio Escalera author Yunlian Sun author Haiqing Li author Zhenan Sun author Thomas B. Moeslund author Modris Greitans author 2016 Reliable facial recognition systems are of crucial importance in various applications from entertainment to security. Thanks to the deep-learning concepts introduced in the field, a significant improvement in the performance of the unimodal facial recognition systems has been observed in the recent years. At the same time a multimodal facial recognition is a promising approach. This study combines the latest successes in both directions by applying deep learning convolutional neural networks (CNN) to the multimodal RGB, depth, and thermal (RGB-D-T) based facial recognition problem outperforming previously published results. Furthermore, a late fusion of the CNN-based recognition block with various hand-crafted features (local binary patterns, histograms of oriented gradients, Haar-like rectangular features, histograms of Gabor ordinal measures) is introduced, demonstrating even better recognition performance on a benchmark RGB-D-T database. The obtained results in this study show that the classical engineered features and CNN-based features can complement each other for recognition purposes. HuPBA;MILAB; exported from refbase (http://refbase.cvc.uab.es/show.php?record=2854), last updated on Thu, 27 Apr 2023 13:18:23 +0200 text https://ieeexplore.ieee.org/document/7746050 https://ieeexplore.ieee.org/document/7746050 Admin @ si @ OCN2016 2016 continuing periodical academic journal 5 4 297 303