Unimodal approaches for emotion recognition: A systematic review

被引:2
|
作者
Tomar, Pragya Singh [1 ]
Mathur, Kirti [2 ]
Suman, Ugrasen [1 ]
机构
[1] Devi Ahilya Univ, Sch Comp Sci & Informat Technol SCSIT, Indore, MP, India
[2] Devi Ahilya Univ, Int Inst Profess Studies IIPS, Indore, MP, India
来源
关键词
Emotion recognition; Affective computing; Human -computer interaction; Systematic literature review; FACIAL EXPRESSION RECOGNITION; SENTIMENT; MODEL; FEATURES; FUTURE; FACE;
D O I
10.1016/j.cogsys.2022.10.012
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Affective computing is a rising interdisciplinary field of research spanning the areas from artificial intelligence, natural language processing to cognitive and social sciences. Potential applications comprise of man-machine interaction, healthcare, entertainment, teaching, marketing and many more. Despite the increasing number of papers published in the domains of affective computing, emotion recognition, and human-computer interaction (HCI), there are still gaps in the comprehensive literature review that covers all relevant studies in a single study, which this review attempts to address. As a result, this study provides a systematic literature review (SLR) on existing modalities (unimodals) for emotion recognition, emotion models, and trends in relevant studies by selecting articles published from January 2010 to June 2021. To ensure the retrieval of all relevant studies, a review protocol is used that includes both automatic and manual searches. Based on the research questions, the final 129 papers are reviewed and relevant information is extracted. This SLR provides future research directions to assist novice researchers and practitioners in more efficiently utilizing affective computing techniques.
引用
收藏
页码:94 / 109
页数:16
相关论文
共 50 条
  • [1] Speech emotion recognition approaches: A systematic review
    Hashem, Ahlam
    Arif, Muhammad
    Alghamdi, Manal
    [J]. SPEECH COMMUNICATION, 2023, 154
  • [2] A systematic literature review of speech emotion recognition approaches
    Singh, Youddha Beer
    Goel, Shivani
    [J]. NEUROCOMPUTING, 2022, 492 : 245 - 263
  • [3] Emotion recognition from unimodal to multimodal analysis: A review
    Ezzameli, K.
    Mahersia, H.
    [J]. INFORMATION FUSION, 2023, 99
  • [4] Emotion recognition study: systematic review
    Telaska, Tatiele dos Santos
    Caron, Lilian
    Jaworski de Sa Riechi, Tatiana Izabele
    [J]. CUADERNOS DE NEUROPSICOLOGIA-PANAMERICAN JOURNAL OF NEUROPSYCHOLOGY, 2020, 14 (03): : 75 - 85
  • [5] Multiple feature fusion for unimodal emotion recognition
    Yang Lingzhi
    Ban Xiaojuan
    Michele Mukeshimana
    Chen Zhe
    [J]. The Journal of China Universities of Posts and Telecommunications, 2019, 26 (02) : 17 - 29
  • [6] Multiple feature fusion for unimodal emotion recognition
    Lingzhi, Yang
    Xiaojuan, Ban
    Mukeshimana, Michele
    Zhe, Chen
    [J]. Journal of China Universities of Posts and Telecommunications, 2019, 26 (02): : 17 - 29
  • [7] Facial emotion recognition in aging: a systematic review
    Ferreira, Cyntia Diogenes
    Torro-Alves, Nelson
    [J]. UNIVERSITAS PSYCHOLOGICA, 2016, 15 (05)
  • [8] Human emotion recognition using intelligent approaches: A review
    Chowdary, M. Kalpana
    Hemanth, D. Jude
    [J]. INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS, 2019, 13 (04): : 417 - 433
  • [9] The effect of unimodal affective priming on dichotic emotion recognition
    Voyer, Daniel
    Myles, Daniel
    [J]. LATERALITY, 2018, 23 (05): : 517 - 537
  • [10] Automatic Speech Emotion Recognition: a Systematic Literature Review
    Mustafa H.H.
    Darwish N.R.
    Hefny H.A.
    [J]. International Journal of Speech Technology, 2024, 27 (1) : 267 - 285