%0 Journal Article %T Improved RGB-D-T based Face Recognition %A Marc Oliu %A Ciprian Corneanu %A Kamal Nasrollahi %A Olegs Nikisins %A Sergio Escalera %A Yunlian Sun %A Haiqing Li %A Zhenan Sun %A Thomas B. Moeslund %A Modris Greitans %J IET Biometrics %D 2016 %V 5 %N 4 %F Marc Oliu2016 %O HuPBA;MILAB; %O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2854), last updated on Thu, 27 Apr 2023 13:18:23 +0200 %X 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. %U https://ieeexplore.ieee.org/document/7746050 %P 297-303