A comprehensive review of facial expression recognition techniques

被引:29
作者
Adyapady, R. Rashmi [1 ]
Annappa, B. [1 ]
机构
[1] Natl Inst Technol Karnataka, Dept Comp Sci & Engn, Surathkal, India
关键词
Facial expression recognition; Emotion recognition; Machine and deep learning; Constrained environment; Unconstrained environment; EMOTION RECOGNITION; AUTOMATIC RECOGNITION; DEEP; NETWORK; ATTENTION; FEATURES; ROBUST; WILD; ENGAGEMENT; FUSION;
D O I
10.1007/s00530-022-00984-w
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Emotion recognition has opened up many challenges, which lead to various advances in computer vision and artificial intelligence. The rapid development in this field has encouraged the development of an automatic system that could accurately analyze and measure the emotions of human beings via facial expressions. This study mainly focuses on facial expression recognition from visual cues, as visual information is the most prominent channel for social communication. The paper provides a comprehensive review of recent advancements in algorithm development, presents the overall findings performed over the past decades, discusses their advantages and constraints. It explores the transition from the laboratory-controlled environment to challenging real-world (in-the-wild) conditions, focusing on essential issues that require further exploration. Finally, relevant opportunities in this field, challenges, and future directions mentioned in this paper assist the researchers and academicians in designing efficient and robust facial expression recognition systems.
引用
收藏
页码:73 / 103
页数:31
相关论文
共 129 条
  • [61] Lucey P, 2010, IEEE COMP SOC C COMP, P94, DOI 10.1109/CVPRW.2010.5543262
  • [62] A novel 2D and 3D multimodal approach for in-the-wild facial expression recognition
    Ly, Thai Son
    Do, Nhu-Tai
    Kim, Soo-Hyung
    Yang, Hyung-Jeong
    Lee, Guee-Sang
    [J]. IMAGE AND VISION COMPUTING, 2019, 92
  • [63] Audio-visual emotion fusion (AVEF): A deep efficient weighted approach
    Ma, Yaxiong
    Hao, Yixue
    Chen, Min
    Chen, Jincai
    Lu, Ping
    Kosir, Andrej
    [J]. INFORMATION FUSION, 2019, 46 : 184 - 192
  • [64] Automatic Facial Expression Recognition System Using Deep Network-Based Data Fusion
    Majumder, Anima
    Behera, Laxmidhar
    Subramanian, Venkatesh K.
    [J]. IEEE TRANSACTIONS ON CYBERNETICS, 2018, 48 (01) : 103 - 114
  • [65] Automatic Analysis of Facial Actions: A Survey
    Martinez, Brais
    Valstar, Michel F.
    Jiang, Bihan
    Pantic, Maja
    [J]. IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, 2019, 10 (03) : 325 - 347
  • [66] A COMPARISON OF 2 SYSTEMS THAT CODE INFANT AFFECTIVE EXPRESSION
    MATIAS, R
    COHN, JF
    ROSS, S
    [J]. DEVELOPMENTAL PSYCHOLOGY, 1989, 25 (04) : 483 - 489
  • [67] Extended DISFA Dataset: Investigating Posed and Spontaneous Facial Expressions
    Mavadati, Mohammad
    Sanger, Peyten
    Mahoor, Mohammad H.
    [J]. PROCEEDINGS OF 29TH IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, (CVPRW 2016), 2016, : 1452 - 1459
  • [68] DISFA: A Spontaneous Facial Action Intensity Database
    Mavadati, S. Mohammad
    Mahoor, Mohammad H.
    Bartlett, Kevin
    Trinh, Philip
    Cohn, Jeffrey F.
    [J]. IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, 2013, 4 (02) : 151 - 160
  • [69] Min R., 2011, Proc. IEEE International Conference on Automatic Face Gesture Recognition and Workshops, P442, DOI DOI 10.1109/FG.2011.5771439
  • [70] Efficient Detection of Occlusion prior to Robust Face Recognition
    Min, Rui
    Hadid, Abdenour
    Dugelay, Jean-Luc
    [J]. SCIENTIFIC WORLD JOURNAL, 2014,