Automatic emotion recognition : learning from imperfect data

Abstract

Current emotion recognition models suffer from data imperfections problems, leading to relatively low performance and biased decisions. This thesis addresses two data imperfections — imperfect annotations and imperfect samples — individually and jointly to improve the performance and fairness of facial emotion recognition models. 1. Imperfect Annotations. The lack of databases annotated with all three commonly used emotion descriptors, i.e., facial action units, categorical emotions, and valence-arousal, has hindered multi-task emotion model development. We proposed two approaches to solve this problem: a data-driven approach and a knowledge-aware approach. Both approaches outperformed previous single-task and multi-task models, emphasizing the importance of learning the relationship between tasks. 2. Imperfect Samples. We addressed two challenges in predicting valence and arousal for videos under unconstrained light variations. First, varying illumination conditions require a robust motion representation. Second, the dynamics of facial expressions are difficult to capture. For the first challenge, we proposed to use phase differences instead of optical flow as the motion features. For the second challenge, we designed a two-stream network to learn the motion features from two durations corresponding to micro- and macro-expressions. Experimental results showed that phase differences are more robust than optical flow to illumination changes. 3. Imperfect Samples and Annotations. Many emotion datasets have two biases: composition bias related to data distribution and annotation bias related to annotators’ prejudice. We proposed a two-stage training method to mitigate composition bias in the first stage via disentanglement and mitigate annotation bias in the second stage via a similarity constraint. Our method showed superior performance to methods targeting only one type of bias. Our proposed methods may also be applicable in other applications, where inter-task relationships, the robustness of motion features, and fair representations are of concern. Future directions suggested by our work include emotion uncertainty prediction and causal inference of emotions.

Date of Award	2022
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	Bertram Emil SHI (Supervisor)