Multimodal Affective Computing

Multimodal Affective Computing is a computational framework within affective computing that integrates multiple modalities—such as facial expressions, vocal prosody, physiological signals, and behavioral cues—to recognize, interpret, and respond to learners’ emotional states in educational contexts with greater accuracy and robustness than unimodal approaches.

533

Publications

36.4K

Citations

1.8K

Authors

597

Institutions

Publications per year

2017–2026

363

Authors

1.8K

  1. BW
    Björn W. Schuller

    Imperial College London, United Kingdom

    23 publications · 1.5K citations

    16

    h-index

  2. EC
    Erik Cambria

    Nanyang Technological University, Singapore

    17 publications · 2.5K citations

    15

    h-index

  3. BL
    Bao‐Liang Lu

    Shanghai Jiao Tong University, China

    9 publications · 2K citations

    9

    h-index

  4. MP
    Maja Pantić

    Imperial College London, United Kingdom

    9 publications · 2.8K citations

    9

    h-index

  5. WZ
    Wei‐Long Zheng

    Shanghai Jiao Tong University, China

    7 publications · 2K citations

    7

    h-index

1–5 of 1.8K

Venues

  1. IEEE Transactions on Affective Computing

    TAC · 25 publications · 4.6K citations

    22

    h-index

  2. Sensors

    12 publications · 480 citations

    11

    h-index

  3. Multimedia Tools and Applications

    11 publications · 465 citations

    11

    h-index

  4. arXiv (Cornell University)

    Repository · 16 publications · 366 citations

    11

    h-index

  5. Neurocomputing

    10 publications · 513 citations

    10

    h-index

1–5