Multimodal Affect Analysis

Multimodal Affect Analysis is a computational framework that integrates and interprets affective cues from multiple modalities—such as facial expressions, vocal prosody, physiological signals, and textual language—to recognize and model human emotions with greater accuracy and robustness than unimodal approaches.

127

Publications

15.1K

Citations

387

Authors

156

Institutions

Publications per year

2017–2026

62

Authors

387

  1. BW
    Björn W. Schuller

    Imperial College London, United Kingdom

    7 publications · 495 citations

    9

    h-index

  2. TP
    Thierry Pun

    University of Geneva, Switzerland

    5 publications · 5K citations

    5

    h-index

  3. NS
    Nicu Sebe

    University of Trento, Italy

    5 publications · 726 citations

    5

    h-index

  4. EC
    Erik Cambria

    Nanyang Technological University, Singapore

    3 publications · 70 citations

    5

    h-index

  5. MP
    Maja Pantić

    Imperial College London, United Kingdom

    5 publications · 1.9K citations

    5

    h-index

1–5 of 387

Venues

  1. IEEE Transactions on Affective Computing

    TAC · 18 publications · 9K citations

    17

    h-index

  2. arXiv (Cornell University)

    Repository · 8 publications · 172 citations

    8

    h-index

  3. Information Fusion

    3 publications · 139 citations

    4

    h-index

  4. Journal on Multimodal User Interfaces

    2 publications · 39 citations

    2

    h-index

  5. MediaEval

    2 publications · 32 citations

    2

    h-index

1–5