Learning Deep Hierarchical Features with Spatial Regularization for One-Class Facial Expression Recognition


  • Bingjun Luo Tsinghua University
  • Junjie Zhu Tsinghua University
  • Tianyu Yang Tsinghua University
  • Sicheng Zhao Tsinghua University
  • Chao Hu Central South University
  • Xibin Zhao Tsinghua University
  • Yue Gao Tsinghua University




HAI: Human-Computer Interaction, HAI: Emotional Intelligence, DMKM: Anomaly/Outlier Detection, APP: Humanities & Computational Social Science, CMS: Social Cognition and Interaction, ML: Other Foundations of Machine Learning


Existing methods on facial expression recognition (FER) are mainly trained in the setting when multi-class data is available. However, to detect the alien expressions that are absent during training, this type of methods cannot work. To address this problem, we develop a Hierarchical Spatial One Class Facial Expression Recognition Network (HS-OCFER) which can construct the decision boundary of a given expression class (called normal class) by training on only one-class data. Specifically, HS-OCFER consists of three novel components. First, hierarchical bottleneck modules are proposed to enrich the representation power of the model and extract detailed feature hierarchy from different levels. Second, multi-scale spatial regularization with facial geometric information is employed to guide the feature extraction towards emotional facial representations and prevent the model from overfitting extraneous disturbing factors. Third, compact intra-class variation is adopted to separate the normal class from alien classes in the decision space. Extensive evaluations on 4 typical FER datasets from both laboratory and wild scenarios show that our method consistently outperforms state-of-the-art One-Class Classification (OCC) approaches.




How to Cite

Luo, B., Zhu, J., Yang, T., Zhao, S., Hu, C., Zhao, X., & Gao, Y. (2023). Learning Deep Hierarchical Features with Spatial Regularization for One-Class Facial Expression Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 37(5), 6065-6073. https://doi.org/10.1609/aaai.v37i5.25749



AAAI Technical Track on Humans and AI