Towards uncertainty-aware and label-efficient machine learning of human expressive behaviour

The ability to recognise emotional expressions from non-verbal behaviour plays a key role in human-human interaction. Endowing machines with the same ability is critical to enriching human-computer interaction. Despite receiving widespread attention so far, human-level automatic recognition of affective expressions is still an elusive task for machines. Towards improving the current state of machine learning methods applied to affect recognition, this thesis identifies two challenges: label ambiguity and label scarcity. Firstly, this thesis notes that it is difficult to establish a clear one-to-one mapping between inputs (face images or speech segments) and their target emotion labels, considering that emotion perception is inherently subjective. As a result, the problem of label ambiguity naturally arises in the manual annotations of affect. Ignoring this fundamental problem, most existing affect recognition methods implicitly assume a one-to-one input-target mapping and use deterministic function learning. In contrast, this thesis proposes to learn non-deterministic functions based on uncertainty-aware probabilistic models, as they can naturally accommodate the one-to-many input-target mapping. Besides improving the affect recognition performance, the proposed uncertainty-aware models in this thesis demonstrate three important applications: adaptive multimodal affect fusion, human-in-the-loop learning of affect, and improved performance on downstream behavioural analysis tasks like personality traits estimation. Secondly, this thesis aims to address the challenge of scarcity of affect labelled datasets, caused by the cumbersome and time-consuming nature of the affect annotation process. To this end, this thesis notes that audio and visual feature encoders used in the existing models are label-inefficient i.e. learning them requires large amounts of labelled training data. As a solution, this thesis proposes to pre-train the feature encoders using unlabelled data to make them more label-efficient i.e. using as few labelled training examples as possible to achieve good emotion recognition performance. A novel self-supervised pre-training method is proposed in this thesis by posing hand-engineered emotion features as task-specific representation learning priors. By leveraging large amounts of unlabelled audiovisual data, the proposed self-supervised pre-training method demonstrates much better label efficiency compared to the commonly employed pre-training methods

Towards uncertainty-aware and label-efficient machine learning of human expressive behaviour

The ability to recognise emotional expressions from non-verbal behaviour plays a key role in human-human interaction. Endowing machines with the same ability is critical to enriching human-computer interaction. Despite receiving widespread attention so far, human-level automatic recognition of affective expressions is still an elusive task for machines. Towards improving the current state of machine learning methods applied to affect recognition, this thesis identifies two challenges: label ambiguity and label scarcity. Firstly, this thesis notes that it is difficult to establish a clear one-to-one mapping between inputs (face images or speech segments) and their target emotion labels, considering that emotion perception is inherently subjective. As a result, the problem of label ambiguity naturally arises in the manual annotations of affect. Ignoring this fundamental problem, most existing affect recognition methods implicitly assume a one-to-one input-target mapping and use deterministic function learning. In contrast, this thesis proposes to learn non-deterministic functions based on uncertainty-aware probabilistic models, as they can naturally accommodate the one-to-many input-target mapping. Besides improving the affect recognition performance, the proposed uncertainty-aware models in this thesis demonstrate three important applications: adaptive multimodal affect fusion, human-in-the-loop learning of affect, and improved performance on downstream behavioural analysis tasks like personality traits estimation. Secondly, this thesis aims to address the challenge of scarcity of affect labelled datasets, caused by the cumbersome and time-consuming nature of the affect annotation process. To this end, this thesis notes that audio and visual feature encoders used in the existing models are label-inefficient i.e. learning them requires large amounts of labelled training data. As a solution, this thesis proposes to pre-train the feature encoders using unlabelled data to make them more label-efficient i.e. using as few labelled training examples as possible to achieve good emotion recognition performance. A novel self-supervised pre-training method is proposed in this thesis by posing hand-engineered emotion features as task-specific representation learning priors. By leveraging large amounts of unlabelled audiovisual data, the proposed self-supervised pre-training method demonstrates much better label efficiency compared to the commonly employed pre-training methods

Are 3D face shapes expressive enough for recognising continuous emotions and action unit intensities?

Recognising continuous emotions and action unit (AU) intensities from face videos requires a spatial and temporal understanding of expression dynamics. Existing works primarily rely on 2D face appearances to extract such dynamics. This work focuses on a promising alternative based on parametric 3D face shape alignment models, which disentangle different factors of variation, including expression-induced shape variations. We aim to understand how expressive 3D face shapes are in estimating valence-arousal and AU intensities compared to the state-of-the-art 2D appearance-based models. We benchmark four recent 3D face alignment models: ExpNet, 3DDFA-V2, DECA, and EMOCA. In valence-arousal estimation, expression features of 3D face models consistently surpassed previous works and yielded an average concordance correlation of .739 and .574 on SEWA and AVEC 2019 CES corpora, respectively. We also study how 3D face shapes performed on AU intensity estimation on BP4D and DISFA datasets, and report that 3D face features were on par with 2D appearance features in AUs 4, 6, 10, 12, and 25, but not the entire set of AUs. To understand this discrepancy, we conduct a correspondence analysis between valence-arousal and AUs, which points out that accurate prediction of valence-arousal may require the knowledge of only a few AUs.Comment: Accepted to IEEE Transactions on Affective Computin