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A depth video-based facial expression recognition system using radon transform, generalized discriminant analysis, and hidden Markov model

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Abstract

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.

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Acknowledgements

The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University for its funding of this research through the Research Group Project no RGP- VPP-281.

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Authors and Affiliations

  1. Department of Computer Education, Sungkyunkwan University, Seoul, Republic of Korea

    Md. Zia Uddin

  2. Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, KSA, Saudi Arabia

    Mohammad Mehedi Hassan

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  1. Md. Zia Uddin
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  2. Mohammad Mehedi Hassan
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Correspondence to Mohammad Mehedi Hassan.

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Uddin, M.Z., Hassan, M.M. A depth video-based facial expression recognition system using radon transform, generalized discriminant analysis, and hidden Markov model. Multimed Tools Appl 74, 3675–3690 (2015). https://doi.org/10.1007/s11042-013-1793-1

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