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

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.

Authors

• 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

Keywords

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