Multimodal Emotion Recognition

Multimodal Emotion Recognition is a computational paradigm that integrates multiple heterogeneous data modalities—such as facial expressions, vocal prosody, physiological signals, and textual content—to identify and classify emotional states, distinguishing itself from unimodal approaches by leveraging complementary and synergistic information across modalities for improved recognition accuracy and robustness.

2.4K

Publications

157.8K

Citations

6.6K

Authors

1.6K

Institutions

Publications per year

2017–2026

1.6K

Variants

Multimodal Affective Recognition

Authors

6.6K

  1. BW
    Björn W. Schuller

    Imperial College London, United Kingdom

    82 publications · 8.2K citations

    43

    h-index

  2. JT
    Jianhua Tao

    Chinese Academy of Sciences, China

    29 publications · 1.7K citations

    24

    h-index

  3. EC
    Erik Cambria

    Nanyang Technological University, Singapore

    31 publications · 4.6K citations

    24

    h-index

  4. BL
    Bao‐Liang Lu

    Shanghai Jiao Tong University, China

    25 publications · 3.4K citations

    21

    h-index

  5. MP
    Maja Pantić

    Imperial College London, United Kingdom

    23 publications · 5.8K citations

    20

    h-index

1–5 of 6.6K

Venues

  1. IEEE Transactions on Affective Computing

    TAC · 89 publications · 15.2K citations

    50

    h-index

  2. IEEE Access

    63 publications · 3.7K citations

    34

    h-index

  3. Sensors

    48 publications · 2.7K citations

    31

    h-index

  4. Information Fusion

    35 publications · 3.9K citations

    31

    h-index

  5. Neurocomputing

    41 publications · 3K citations

    30

    h-index

1–5