The paper audits five AffectNet‑pretrained facial affect models on child faces, revealing that all models perform worse on naturalistic child images and consistently confuse fear with surprise. The degradation is linked to specific expression morphologies—particularly open‑mouth faces—and to South‑Asian children, rather than skin tone. A linear probe shows that most of the gap lies in the classifier head, and fine‑tuning just this head on a small amount of child data improves performance by up to 28% on the largest child datasets.
arXiv:2609.36563v1 Announce Type: new Abstract: Visual emotion recognition commonly assumes that all evidence required for prediction is contained in the observed image or video. Yet the same visible...
arXiv:2603. 03989v2 Announce Type: replace-cross Abstract: When visual evidence is ambiguous, vision models must decide how to interpret face-like patterns.
arXiv:2609.05540v2 Announce Type: replace-cross Abstract: Many medical conditions require clinical assessment of symptoms, history, and functioning rather than judgments from appearance. We study whe...
arXiv:2602.07815v3 Announce Type: replace Abstract: Facial age estimation plays a critical role in content moderation, age verification, and deepfake detection. However, no prior benchmark has system...
The study audits eight text-to-image models (four Western, four Chinese) by generating 56,000 synthetic faces under neutral and six emotion prompts. It finds pervasive demographic biases: young and White-coded faces are overrepresented, while combinations like young female Black faces are nearly absent. Emotion prompts further skew outputs, with negative emotions favoring White, middle-aged, male-coded faces and lower perceived attractiveness. The research highlights the need for intersectional, emotion‑conditioned, multilingual audits before deploying such models.
arXiv:2606. 00670v1 Announce Type: cross Abstract: Face-to-face speech comprehension is inherently multimodal, integrating acoustic signals with visible articulation, facial expression, head motion, and other socially relevant cues.
arXiv:2607. 03581v1 Announce Type: cross Abstract: The widespread adoption of facial masks, accelerated by COVID-19 and mandated in security-sensitive settings, has exposed limitations of conventional face recognition systems.
WELD is the first dataset that combines long‑term (30.1 months), naturalistic workplace recordings, a stable small‑team social structure, and a fully passive sensing protocol approved by institutional review boards. It contains 733,780 per‑frame seven‑class facial‑expression probability vectors from 49 employees of a Chinese software company, making it the longest in‑the‑wild emotion corpus that supports both within‑individual longitudinal and within‑team relational analyses. The authors validate the corpus by reproducing known affective phenomena and report four novel findings, including variance decomposition of daily valence, hidden Markov emotional regimes, turnover prediction metrics, and systematic over‑prediction of “angry” on neutral Asian faces by an off‑the‑shelf FER model.
The paper introduces EmoNet‑Face‑HQ, a fine‑grained emotion recognition benchmark that uses generated portraits and a 40‑category taxonomy to evaluate vision‑language models (VLMs). It finds that VLMs perform poorly when asked to generate responses but can match or surpass a fine‑tuned model (Empathic‑Insight‑Face) when their logits are read directly as binary queries. The study shows that the benchmark’s difficulty lies in the readout process rather than in perception, and that graded probability outputs yield better performance than simple yes/no questions.
arXiv:2608. 14130v1 Announce Type: cross Abstract: Computer vision models for generated facial content, such as face editing and privacy protection, increasingly affect people, requiring similarity metrics that serve as faithful proxies for human perception.
arXiv:2409. 00240v2 Announce Type: replace-cross Abstract: Automatic facial action unit (AU) recognition is used widely in facial expression analysis.
Sofroniew et al. (2026) showed that emotion concepts in Claude Sonnet 4.5 are encoded as vectors whose geometry mirrors human affect psychology. This study replicates that finding using the base pretrained model google/gemma-2-27b, generating 205,200 Claude Sonnet 4.5 stories, extracting 171 emotion vectors, and recovering a similar affective circumplex with principal components explaining comparable variance. The analysis further identifies a sharp geometric seam at layers 22‑26, demonstrates that much of the geometry already exists in static token embeddings, and shows that the geometry predicts token‑level co‑activation with high correlation.