Weakly Supervised Learning for Facial Affective Behavior Analysis : A Review

Recent advances in deep learning (DL) and computational capacity have enabled facial affective behavior analysis (FABA) to progress from static images captured in controlled settings to fine-grained analysis of facial expressions in real-world video data. However, training accurate DL models for FABA typically requires large-scale, expert-annotated datasets, which are costly to obtain and inherently noisy due to the ambiguity of labeling subtle facial expressions and action units (AUs). To mitigate these challenges, weakly supervised learning (WSL) has emerged as a promising paradigm for training models with weak annotations. In this paper, we present a structured taxonomy of WSL scenarios for FABA, organized according to the type of weak annotation and the specific affective task. Building on this taxonomy, we provide a critical synthesis of representative WSL methods for both classification (expression and AU recognition) and regression (expression and AU intensity estimation) tasks, focusing on their core methodological ideas, strengths, and limitations. Furthermore, we systematically summarize the comparative performance of WSL approaches along with widely adopted experimental setups and evaluation protocols. Our critical assessment identifies key challenges and future research directions, including the need for efficient adaptation of foundation models and for the development of robust, scalable FABA systems suitable for real-world applications.