Adversarial Feature Distillation for Facial Expression Recognition

Mengchao Bai, Xi Jia, Weicheng Xie, Linlin Shen

Abstract: Human face image contains abundant information including expression, age and gender, etc. Therefore, extracting discriminative feature for certain attribute while expelling others is critical for single facial attribute analysis. In this paper, we propose an adversarial facial expression recognition system, named expression distilling and dispelling learning (ED $$^2$$ L), to extract discriminative expression feature from a given face image. The proposed ED $$^{2}$$ L framework composed of two branches, i.e. expression distilling branch ED $$^{2}$$ L-t and expression dispelling branch ED $$^{2}$$ L-p. The ED $$^{2}$$ L-t branch aims to extract the expression-related feature, while the ED $$^{2}$$ L-p branch extracts the non-related feature. The disentangled features jointly serve as a complete representation of the face. Extensive experiments on several benchmark databases, i.e. the CK+, MMI, BU-3DFE and Oulu-CASIA, demonstrate the effectiveness of the proposed ED $$^{2}$$ L framework.