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, i.e., facial 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., finegrained 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 taskspecific state-of-the-art models.
The FABA-Instruct dataset is designed to enhance facial action unit (AU) and emotion recognition. It includes 19,877 images with detailed annotations and instructions. Unlike existing datasets, FABA-Instruct provides both categorical and descriptive annotations, making it a valuable resource for training advanced AI models.
The distribution of FABA-Instruct on AU (a) and emotion (b) tasks, and the word clouds (c).
The facial action units (AUs) visible in this image suggest a variety of facial muscle movements. Here's a breakdown:
The overall configuration of these facial action units seems to create an intense or exaggerated expression, which could be associated with strong emotions such as excitement, aggression, or intense joy.
In this image, we can analyze the facial expression by looking at the action units (AUs), which are part of the Facial Action Coding System (FACS):
Based on these AUs, the expression can be interpreted as a happy or content one, commonly associated with a genuine smile (also known as a Duchenne smile). This type of smile typically involves both the muscles around the eyes and the mouth, which are activated in this facial expression.
The person in the image appears to be showing an exaggerated expression of discomfort or unease. Their eyes are wide open, which can suggest surprise, shock, or fear, while their mouth is turned downward in a pronounced frown, which often indicates sadness or displeasure. The overall expression could be interpreted as a humorous display of distaste or dissatisfaction.
The person in the image appears to have a neutral expression with a slight hint of a smile. There is relaxed composure around the eyes and mouth, suggesting a calm or content state. The eyes are engaged and there is a mild pleasantness to the overall demeanor.
The emotion depicted on the face in the image appears to be a complex one, often characterized as a mixture of sadness and discomfort, potentially with an element of a forced smile. The downturned eyebrows, elevated cheeks, and the slight pulling up of the lip corners suggest a pained expression that could be associated with crying or distress, yet there is an attempt to smile. This could indicate a social smile or an attempt to mask one's true feelings.
The EmoLA model enhances facial expression analysis through instruction tuning, improving recognition of facial action units (AUs) and emotions. It combines categorical and descriptive annotations, providing robust performance and interpretability in affective behavior analysis.
FABA-Bench is a new benchmark introduced for the evaluation of models in Facial Affective Behavior Analysis (FABA). This benchmark is designed to assess both the recognition and generation capabilities of models on instruction-following data. The key metric used in FABA-Bench is the REGE score (Recognition and Generation Evaluation), which combines recognition accuracy with the quality of text generation. FABA-Bench provides a comprehensive evaluation framework that highlights the strengths and weaknesses of different models in understanding and generating descriptions for complex facial affective behaviors.
@article{li2024facial,
title={Facial Affective Behavior Analysis with Instruction Tuning},
author={Li, Yifan and Dao, Anh and Bao, Wentao and Tan, Zhen and Chen, Tianlong and Liu, Huan and Kong, Yu},
journal={European Conference on Computer Vision (ECCV) 2024},
year={2024}
}