caa a22 4500 605447578 CHVBK 20210128100132.0 cr unu---uuuuu 210128e20150601xx s 000 0 eng 10.1007/s11042-013-1793-1 doi (NATIONALLICENCE)springer-10.1007/s11042-013-1793-1 A depth video-based facial expression recognition system using radon transform, generalized discriminant analysis, and hidden Markov model [Elektronische Daten] [Md. Uddin, Mohammad Hassan] In this paper, a depth camera-based novel method is proposed to recognize several facial expressions from depth video. At first, Radon Transformation (RT) is done to extract features from the time-sequential depth faces that are further improved by Generalized Discriminant Analysis (GDA) to generate more robust features and then, Hidden Markov Models (HMMs) are applied to train and recognize different facial expressions successfully. Performance of the proposed facial expression recognition shows the superiority over conventional RGB camera-based approaches. Springer Science+Business Media New York, 2013 Facial expression recognition nationallicence Depth video nationallicence Radon transformation nationallicence Uddin Md Department of Computer Education, Sungkyunkwan University, Seoul, Republic of Korea aut Hassan Mohammad Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, KSA, Saudi Arabia aut Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/11(2015-06-01), 3675-3690 1380-7501 74:11<3675 2015 74 11042 https://doi.org/10.1007/s11042-013-1793-1 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11042-013-1793-1 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Uddin Md Department of Computer Education, Sungkyunkwan University, Seoul, Republic of Korea aut NATIONALLICENCE 700 1- Hassan Mohammad Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, KSA, Saudi Arabia aut NATIONALLICENCE 773 0- Multimedia Tools and Applications Springer US; http://www.springer-ny.com 74/11(2015-06-01), 3675-3690 1380-7501 74:11<3675 2015 74 11042