Oct 2026· Companion Publication of the 28th International Conference on Multimodal Interaction· 0 citations· 30 references
TL;DR
Gender differences in non-verbal public-speaking expressivity through Facial Action Unit (AU) statistics and upper-body posture and movement cues are investigated and postural and dynamic gender-associated patterns in public speaking are revealed.
Abstract
Using the 3MT_French dataset of three-minute thesis pitches, we investigate gender differences in non-verbal public-speaking expressivity through Facial Action Unit (AU) statistics and upper-body posture and movement cues. Four classifiers (RF, XGBoost, SVC, LR) trained under 10-fold stratified cross-validation reach best F1-scores of 0.72 for face-only and 0.62 for body-only, both with XGBoost. A SHAP-based feature ranking combined with FDR-corrected non-parametric tests reveals a two-axis structure: male speakers show a sustained baseline of activity in articulation-related AUs (notably Lip Pressor AU24) and a broader upper-body posture, while female speakers display greater dynamic facial expressivity and higher peak smile intensity (Lip Corner Puller AU12). While partially aligned with prior work, these findings extend known patterns to fine-grained AU statistics and reveal postural and dynamic gender-associated patterns in public speaking. Cross-corpus replication remains a key next step.
Deep learning has achieved strong predictive performance in facial emotion recognition (FER), but interpreting the internal representations underlying model decisions remains challenging. In this study, we propose a cross-representation concept-based explainability framework that extends TCAV analysis beyond a single v...
Simona Moldovanu, Sanda Florentina Mihalache, Cătălin Anghel et al.· Applied Informatics· 0 citations
This central contribution is a tested explanation suite: for a strong ensemble member, part-masking and counterfactual edits show that its decisions depend on motion-grounded body-region evidence, and this region saliency aligns with rule-based Laban Movement Analysis (LMA) attributes far more than with classical kinem...
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Facial affect recognition is a key component of human-centered AI, enabling systems to respond appropriately to human nonverbal signals. The deployment of deep learning for facial affect recognition is critically hindered by vulnerability to data-driven biases and a lack of transparency. This thesis addresses these cha...
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Professor Ali Jadbabaie describes how AI, digital transformation, climate change, and complex geopolitics are forcing engineers and policymakers to rethink how infrastructure is designed, built, and managed.
MIT News · Artificial Intelligence· news.mit.eduOct 27, 2026
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
