  Facial affective behavior analysis (FABA) is crucial for understanding human
mental states from images. However, traditional approaches primarily deploy
models to discriminate among discrete emotion categories, and lack the fine
granularity and reasoning capability for complex facial behaviors. The advent
of Multi-modal Large Language Models (MLLMs) has been proven successful in
general visual understanding tasks. However, directly harnessing MLLMs for FABA
is challenging due to the scarcity of datasets and benchmarks, neglecting
facial prior knowledge, and low training efficiency. To address these
challenges, we introduce (i) an instruction-following dataset for two FABA
tasks, e.g., emotion and action unit recognition, (ii) a benchmark FABA-Bench
with a new metric considering both recognition and generation ability, and
(iii) a new MLLM "EmoLA" as a strong baseline to the community. Our initiative
on the dataset and benchmarks reveal the nature and rationale of facial
affective behaviors, i.e., fine-grained facial movement, interpretability, and
reasoning. Moreover, to build an effective and efficient FABA MLLM, we
introduce a facial prior expert module with face structure knowledge and a
low-rank adaptation module into pre-trained MLLM. We conduct extensive
experiments on FABA-Bench and four commonly-used FABA datasets. The results
demonstrate that the proposed facial prior expert can boost the performance and
EmoLA achieves the best results on our FABA-Bench. On commonly-used FABA
datasets, EmoLA is competitive rivaling task-specific state-of-the-art models.
