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Facial Expression Recognition Based on Fused Features and Support Vector Machine

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Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023 (AISI 2023)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 184))

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Abstract

Traditional facial expression recognition methods often suffer from low feature dimension and low recognition rates. These methods typically extract features limited to commonly used image features, while overlooking the semantic information of facial expressions. In this paper, we proposed an innovative facial expression recognition method that incorporates a novel calculation method for facial landmark features. These features are combined with Histogram of Oriented Gradients (HOG) and wavelet features using weighted concatenation to form the final feature vector. Support Vector Machines (SVM) are employed for classification. The proposed method achieves an impressive accuracy of 93.4% on the JAFFE dataset.

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Authors and Affiliations

  1. School of Computer and Information Engineering, Harbin University of Commerce, Harbin, 150028, Heilongjiang, China

    Zhi-jie Zhao & Ming-yang Jiao

Authors
  1. Zhi-jie Zhao
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  2. Ming-yang Jiao
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Corresponding author

Correspondence to Ming-yang Jiao .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    AboulElla Hassanien

  2. Port Said University, Port Fouad City, Egypt

    Rawya Y. Rizk

  3. Department of Operations Research and Statistics, The University of Belgrade, Faculty of Organizational Sciences, Belgrade, Serbia

    Dragan Pamucar

  4. Faculty of Science, Helwan University, Cairo, Egypt

    Ashraf Darwish

  5. Shulin District, Fujian University of Technology, New Taipei, Taiwan

    Kuo-Chi Chang

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Zhao, Zj., Jiao, My. (2023). Facial Expression Recognition Based on Fused Features and Support Vector Machine. In: Hassanien, A., Rizk, R.Y., Pamucar, D., Darwish, A., Chang, KC. (eds) Proceedings of the 9th International Conference on Advanced Intelligent Systems and Informatics 2023. AISI 2023. Lecture Notes on Data Engineering and Communications Technologies, vol 184. Springer, Cham. https://doi.org/10.1007/978-3-031-43247-7_45

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