Facial-R1: Aligning Reasoning and Recognition for Facial Emotion Analysis

• URL: http://arxiv.org/abs/2511.10254v1
• Date: Fri, 14 Nov 2025 01:41:43 GMT
• Title: Facial-R1: Aligning Reasoning and Recognition for Facial Emotion Analysis
• Authors: Jiulong Wu, Yucheng Shen, Lingyong Yan, Haixin Sun, Deguo Xia, Jizhou Huang, Min Cao,
• Abstract summary: Facial Emotion Analysis (FEA) extends traditional facial emotion recognition by incorporating explainable, fine-grained reasoning.<n>Recent approaches leverage Vision-Language Models (VLMs) and achieve promising results, but they face two critical limitations.<n>We propose Facial-R1, a three-stage alignment framework that effectively addresses both challenges with minimal supervision.
• Score: 20.372029918328035
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Facial Emotion Analysis (FEA) extends traditional facial emotion recognition by incorporating explainable, fine-grained reasoning. The task integrates three subtasks: emotion recognition, facial Action Unit (AU) recognition, and AU-based emotion reasoning to model affective states jointly. While recent approaches leverage Vision-Language Models (VLMs) and achieve promising results, they face two critical limitations: (1) hallucinated reasoning, where VLMs generate plausible but inaccurate explanations due to insufficient emotion-specific knowledge; and (2) misalignment between emotion reasoning and recognition, caused by fragmented connections between observed facial features and final labels. We propose Facial-R1, a three-stage alignment framework that effectively addresses both challenges with minimal supervision. First, we employ instruction fine-tuning to establish basic emotional reasoning capability. Second, we introduce reinforcement training guided by emotion and AU labels as reward signals, which explicitly aligns the generated reasoning process with the predicted emotion. Third, we design a data synthesis pipeline that iteratively leverages the prior stages to expand the training dataset, enabling scalable self-improvement of the model. Built upon this framework, we introduce FEA-20K, a benchmark dataset comprising 17,737 training and 1,688 test samples with fine-grained emotion analysis annotations. Extensive experiments across eight standard benchmarks demonstrate that Facial-R1 achieves state-of-the-art performance in FEA, with strong generalization and robust interpretability.

Related papers

• EMO-R3: Reflective Reinforcement Learning for Emotional Reasoning in Multimodal Large Language Models [62.3977734456669]
  We propose Reflective Reinforcement Learning for Emotional Reasoning (EMO-R3), a framework designed to enhance the emotional reasoning ability of Multimodal Large Language Models (MLLMs)<n>We introduce Structured Emotional Thinking to guide the model to perform step-by-step emotional reasoning in a structured and interpretable manner, and design a Reflective Emotional Reward that enables the model to re-evaluate its reasoning based on visual-text consistency and emotional coherence.<n>EMO-R3 significantly improves both the interpretability and emotional intelligence of MLLMs, achieving superior performance across multiple visual emotional understanding benchmarks.
  arXiv  Detail & Related papers  (2026-02-27T08:42:52Z)
• E^2-LLM: Bridging Neural Signals and Interpretable Affective Analysis [54.763420895859035]
  We present ELLM2-EEG-to-Emotion Large Language Model, first MLLM framework for interpretable emotion analysis from EEG.<n>ELLM integrates a pretrained EEG encoder with Q-based LLMs through learnable projection layers, employing a multi-stage training pipeline.<n>Experiments on the dataset across seven emotion categories demonstrate that ELLM2-EEG-to-Emotion Large Language Model achieves excellent performance on emotion classification.
  arXiv  Detail & Related papers  (2026-01-11T13:21:20Z)
• A Unified Spoken Language Model with Injected Emotional-Attribution Thinking for Human-like Interaction [50.05919688888947]
  This paper presents a unified spoken language model for emotional intelligence, enhanced by a novel data construction strategy termed Injected Emotional-Attribution Thinking (IEAT)<n>IEAT incorporates user emotional states and their underlying causes into the model's internal reasoning process, enabling emotion-aware reasoning to be internalized rather than treated as explicit supervision.<n> Experiments on the Human-like Spoken Dialogue Systems Challenge (HumDial) Emotional Intelligence benchmark demonstrate that the proposed approach achieves top-ranked performance across emotional trajectory modeling, emotional reasoning, and empathetic response generation.
  arXiv  Detail & Related papers  (2026-01-08T14:07:30Z)
• VidEmo: Affective-Tree Reasoning for Emotion-Centric Video Foundation Models [46.591026037722436]
  We propose a novel affective cues-guided reasoning framework that unifies fundamental attribute perception, expression analysis, and high-level emotional understanding.<n>At the core of our approach is a family of video emotion foundation models (VidEmo), specifically designed for emotion reasoning and instruction-following.<n>We establish a foundational data infrastructure and introduce a emotion-centric fine-grained dataset consisting of 2.1M diverse instruction-based samples.
  arXiv  Detail & Related papers  (2025-11-04T16:31:09Z)
• From Coarse to Nuanced: Cross-Modal Alignment of Fine-Grained Linguistic Cues and Visual Salient Regions for Dynamic Emotion Recognition [7.362433184546492]
  Dynamic Facial Expression Recognition aims to identify human emotions from temporally evolving facial movements.<n>Our method integrates dynamic motion modeling, semantic text refinement, and token-level cross-modal alignment to facilitate the precise localization of emotionally salient features.
  arXiv  Detail & Related papers  (2025-07-16T04:15:06Z)
• Using Vision Language Models to Detect Students' Academic Emotion through Facial Expressions [40.24786235839105]
  Students' academic emotions significantly influence their social behavior and learning performance.<n>Traditional approaches to automatically and accurately analyze these emotions have predominantly relied on supervised machine learning algorithms.<n>This study investigates the potential of Vision-Language Models (VLMs) to analyze students' academic emotions via facial expressions.
  arXiv  Detail & Related papers  (2025-06-12T04:01:26Z)
• Emotion Knowledge Enhancement for Vision Large Language Models: A Self-Verification Approach for High-Quality Emotion Instruction Data Generation [17.94565281111736]
  We propose a self-verification approach with emotion knowledge enhancement (SEKE) to generate high-quality instruction data for emotion analysis.<n>This approach integrates prior human knowledge to VLLM inference, guided by the inherent correlations between three grained levels of emotion descriptions.<n>A self-verification strategy with Uncertainty-Aware Monte Carlo sampling (SV-UAMC) is further embedded to efficiently extract more accurate VLLM predictions.
  arXiv  Detail & Related papers  (2025-05-14T03:00:20Z)
• Disentangle Identity, Cooperate Emotion: Correlation-Aware Emotional Talking Portrait Generation [63.94836524433559]
  DICE-Talk is a framework for disentangling identity with emotion and cooperating emotions with similar characteristics.<n>We develop a disentangled emotion embedder that jointly models audio-visual emotional cues through cross-modal attention.<n>Second, we introduce a correlation-enhanced emotion conditioning module with learnable Emotion Banks.<n>Third, we design an emotion discrimination objective that enforces affective consistency during the diffusion process.
  arXiv  Detail & Related papers  (2025-04-25T05:28:21Z)
• MEMO-Bench: A Multiple Benchmark for Text-to-Image and Multimodal Large Language Models on Human Emotion Analysis [53.012111671763776]
  This study introduces MEMO-Bench, a comprehensive benchmark consisting of 7,145 portraits, each depicting one of six different emotions. Results demonstrate that existing T2I models are more effective at generating positive emotions than negative ones. Although MLLMs show a certain degree of effectiveness in distinguishing and recognizing human emotions, they fall short of human-level accuracy.
  arXiv  Detail & Related papers  (2024-11-18T02:09:48Z)
• Self-supervised Gait-based Emotion Representation Learning from Selective Strongly Augmented Skeleton Sequences [4.740624855896404]
  We propose a contrastive learning framework utilizing selective strong augmentation for self-supervised gait-based emotion representation. Our approach is validated on the Emotion-Gait (E-Gait) and Emilya datasets and outperforms the state-of-the-art methods under different evaluation protocols.
  arXiv  Detail & Related papers  (2024-05-08T09:13:10Z)
• Stimuli-Aware Visual Emotion Analysis [75.68305830514007]
  We propose a stimuli-aware visual emotion analysis (VEA) method consisting of three stages, namely stimuli selection, feature extraction and emotion prediction. To the best of our knowledge, it is the first time to introduce stimuli selection process into VEA in an end-to-end network. Experiments demonstrate that the proposed method consistently outperforms the state-of-the-art approaches on four public visual emotion datasets.
  arXiv  Detail & Related papers  (2021-09-04T08:14:52Z)
• Affective Image Content Analysis: Two Decades Review and New Perspectives [132.889649256384]
  We will comprehensively review the development of affective image content analysis (AICA) in the recent two decades. We will focus on the state-of-the-art methods with respect to three main challenges -- the affective gap, perception subjectivity, and label noise and absence. We discuss some challenges and promising research directions in the future, such as image content and context understanding, group emotion clustering, and viewer-image interaction.
  arXiv  Detail & Related papers  (2021-06-30T15:20:56Z)
• Emotion pattern detection on facial videos using functional statistics [62.997667081978825]
  We propose a technique based on Functional ANOVA to extract significant patterns of face muscles movements. We determine if there are time-related differences on expressions among emotional groups by using a functional F-test.
  arXiv  Detail & Related papers  (2021-03-01T08:31:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.