Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data

Facial Expression Recognition (FER) from videos is a crucial task in various application areas, such as human-computer interaction and health monitoring (e.g., pain, depression, fatigue, and stress). Beyond the challenges of recognizing subtle emotional or health states, the effectiveness of deep FER models is often hindered by the considerable variability of expressions among subjects. Source-free domain adaptation (SFDA) methods are employed to adapt a pre-trained source model using only unlabeled target domain data, thereby avoiding data privacy and storage issues. Typically, SFDA methods adapt to a target domain dataset corresponding to an entire population and assume it includes data from all recognition classes. However, collecting such comprehensive target data can be difficult or even impossible for FER in healthcare applications. In many real-world scenarios, it may be feasible to collect a short neutral control video (displaying only neutral expressions) for target subjects before deployment. These videos can be used to adapt a model to better handle the variability of expressions among subjects. This paper introduces the Disentangled Source-Free Domain Adaptation (DSFDA) method to address the SFDA challenge posed by missing target expression data. DSFDA leverages data from a neutral target control video for end-to-end generation and adaptation of target data with missing non-neutral data. Our method learns to disentangle features related to expressions and identity while generating the missing non-neutral target data, thereby enhancing model accuracy. Additionally, our self-supervision strategy improves model adaptation by reconstructing target images that maintain the same identity and source expression.

Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport

Multimodal affect recognition models have reached remarkable performance in the lab environment due to their ability to model complementary and redundant semantic information. However, these models struggle in the wild, mainly because of the unavailability or quality of modalities used for training. In practice, only a subset of the training-time modalities may be available at test time. Learning with privileged information (PI) enables deep learning models (DL) to exploit data from additional modalities only available during training. State-of-the-art knowledge distillation (KD) methods have been proposed to distill multiple teacher models (each trained on a modality) to a common student model. These privileged KD methods typically utilize point-to-point matching and have no explicit mechanism to capture the structural information in the teacher representation space formed by introducing the privileged modality. We argue that encoding this same structure in the student space may lead to enhanced student performance. This paper introduces a new structural KD mechanism based on optimal transport (OT), where entropy-regularized OT distills the structural dark knowledge. Privileged KD with OT (PKDOT) method captures the local structures in the multimodal teacher representation by calculating a cosine similarity matrix and selects the top-k anchors to allow for sparse OT solutions, resulting in a more stable distillation process. Experiments were performed on two different problems: pain estimation on the Biovid dataset (ordinal classification) and arousal-valance prediction on the Affwild2 dataset (regression). Results show that the proposed method can outperform state-of-the-art privileged KD methods on these problems. The diversity of different modalities and fusion architectures indicates that the proposed PKDOT method is modality and model-agnostic.

Subject-Based Domain Adaptation for Facial Expression Recognition

Adapting a deep learning (DL) model to a specific target individual is a challenging task in facial expression recognition (FER) that may be achieved using unsupervised domain adaptation (UDA) methods. Although several UDA methods have been proposed to adapt deep FER models across source and target data sets, multiple subject-specific source domains are needed to accurately represent the intra- and inter-person variability in subject-based adaption. In this paper, we consider the setting where domains correspond to individuals, not entire datasets. Unlike UDA, multi-source domain adaptation (MSDA) methods can leverage multiple source datasets to improve the accuracy and robustness of the target model. However, previous methods for MSDA adapt image classification models across datasets and do not scale well to a larger number of source domains. In this paper, a new MSDA method is introduced for subject-based domain adaptation in FER. It efficiently leverages information from multiple source subjects (labeled source domain data) to adapt a deep FER model to a single target individual (unlabeled target domain data). During adaptation, our Subject-based MSDA first computes a between-source discrepancy loss to mitigate the domain shift among data from several source subjects. Then, a new strategy is employed to generate augmented confident pseudo-labels for the target subject, allowing a reduction in the domain shift between source and target subjects. Experiments\footnote{\textcolor{red}{\textbf{Supplementary material} contains our code, which will be made public, and additional experimental results.}} on the challenging BioVid heat and pain dataset (PartA) with 87 subjects shows that our Subject-based MSDA can outperform state-of-the-art methods yet scale well to multiple subject-based source domains.