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Facial expression recognition based on hybrid geometry-appearance and dynamic-still feature fusion


Emotion recognition through facial expression is regarded as one of the most effective methods to directly reflect a person’s inner emotional state for affective computing. However, a key issue of facial expression recognition (FER) is how to design and fuse features from videos rapidly and thus extract representative features to improve the recognition accuracy efficaciously. In this paper, we propose a novel expression recognition framework to mitigate this issue. Specifically, we first present a new descriptor, the improved Local Binary Pattern from Three Orthogonal Planes (I-LBP-TOP), which can extract both the static and dynamic features in changing expressions, and set Gabor’s magnitude feature (GMF) as texture information. Meanwhile, the facial landmarks of the peak frame are proposed to represent geometric feature (GF) and the spatiotemporal geometric feature (ST-GF) is obtained by extending it to time dimension. Then we integrate multiple features of image sequences to overcome the limitation of using one single feature descriptor. A support vector machine (SVM) with multiple kernels is applied to train three base classifiers. Finally, to realize reliable expression classification, a decision-level feature fusion method based on a relative majority voting (MV) strategy is also employed. Intensive experiments are conducted on the CK+ and Oulu-CASIA databases, where the experimental results demonstrate that our proposed method achieves an improved performance compared with the existing state-of-the-art hand-crafted approaches.

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This research was supported by National Key R&D Program of China (2018YFC0831503), National Natural Science Foundation of China (61571275), Shenzhen Science and Technology Research and Development Funds (JCYJ20170818104011781).

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Correspondence to Mingqiang Yang or Deqiang Wang.

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Yan, R., Yang, M., Zheng, Q. et al. Facial expression recognition based on hybrid geometry-appearance and dynamic-still feature fusion. Multimed Tools Appl (2022).

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  • Facial expression recognition
  • Gabor feature
  • Geometric feature
  • Feature fusion