Open AccessImproved RGB‐D‐T based face recognition
Author(s) -
Simón Marc Oliu,
Corneanu Ciprian,
Nasrollahi Kamal,
Nikisins Olegs,
Escalera Sergio,
Sun Yunlian,
Li Haiqing,
Sun Zhenan,
Moeslund Thomas B.,
Greitans Modris
Publication year - 2016
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
eISSN - 2047-4946
pISSN - 2047-4938
DOI - 10.1049/iet-bmt.2015.0057
Subject(s) - computer science , artificial intelligence , facial recognition system , pattern recognition (psychology) , convolutional neural network , rgb color model , histogram , deep learning , benchmark (surveying) , block (permutation group theory) , histogram of oriented gradients , computer vision , image (mathematics) , geography , geometry , mathematics , geodesy
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.