Improved RGB-D-T based Face Recognition

Marc Oliu Simon, Ciprian Corneanu, Kamal Nasrollahi, Olegs Nikisins, Sergio Escalera Guerrero, Yunlian Sun, Haiqing Li, Zhenan Sun, Thomas B. Moeslund, Modris Greitans

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

26 Citationer (Scopus)
953 Downloads (Pure)


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 paper combines the latest successes in both directions by applying deep learning Convolutional Neural Networks (CNN) to the multimodal 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 (LBP, HOG, HAAR, HOGOM) is introduced, demonstrating even better recognition performance on a benchmark RGB-D-T database. The obtained results in this paper show that the classical engineered features and CNN-based features can complement each other for recognition purposes.
TidsskriftIET Biometrics
Udgave nummer4
Sider (fra-til)297 - 303
StatusUdgivet - 2016

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