Skip to main content

Advertisement

SpringerLink
Log in

From methods to datasets: a detailed study on facial emotion recognition

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Human ideas and sentiments are mirrored in facial expressions. Facial expression recognition (FER) is a crucial type of visual data that can be utilized to deduce a person’s emotional state. It gives the spectator a plethora of social cues, such as the viewer’s focus of attention, emotion, motivation, and intention. It’s said to be a powerful instrument for silent communication. AI-based facial recognition systems can be deployed at different areas like bus stations, railway stations, airports, or stadiums to help security forces identify potential threats. There has been a lot of research done in this area. But, it lacks a detailed review of the literature that highlights and analyses the previous work in FER (including work on compound emotion and micro-expressions), and a comparative analysis of different models applied to available datasets, further identifying aligned future directions. So, this paper includes a comprehensive overview of different models that can be used in the field of FER and a comparative study of the traditional methods based on hand-crafted feature extraction and deep learning methods in terms of their advantages and disadvantages which distinguishes our work from existing review studies.This paper also brings you to an eye on the analysis of different FER systems, the performance of different models on available datasets, evaluation of the classification performance of traditional and deep learning algorithms in the context of facial emotion recognition which reveals a good understanding of the classifier’s characteristics. Along with the proposed models, this study describes the commonly used datasets showing the year-wise performance achieved by state-of-the-art methods which lacks in the existing manuscripts. At last, the authors itemize recognized research gaps and challenges encountered by researchers which can be considered in future research work.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28

Similar content being viewed by others

Data Availability

All the dataset links are provided in the paper.

References

  1. Shiffrar M, Kaiser MD, Chouchourelou A (2011) Seeing human movement as inherently social. Sci Soc Vis 248–264

  2. Mehrabian A (1981) Silent messages: implicit communication of emotions and attitudes. Wadsworth Pub, Co

    Google Scholar 

  3. Wallbott HG (1998) Bodily expression of emotion. Eur J Soc Psychol 28(6):879–896

    Google Scholar 

  4. De Gelder B (2006) Towards the neurobiology of emotional body language. Nature Rev Neurosci 7(3):242–249

    Google Scholar 

  5. Meeren HK, van Heijnsbergen CC, de Gelder B (2005) Rapid perceptual integration of facial expression and emotional body language. Proc Natl Acad Sci 102(45):16518–16523

    Google Scholar 

  6. Aviezer H, Trope Y, Todorov A (2012) Body cues, not facial expressions, discriminate between intense positive and negative emotions. Science 338(6111):1225–1229

    Google Scholar 

  7. Boyle EA, Anderson AH, Newlands A (1994) The effects of visibility on dialogue and performance in a cooperative problem solving task. Lang Speech 37(1):1–20

    Google Scholar 

  8. Nguyen T, Bass I, Li M, Sethi IK (2005) Investigation of combining svm and decision tree for emotion classification. In: Seventh IEEE International Symposium on Multimedia (ISM’05), p 5. IEEE

  9. Luo Y, Ye J, Adams RB, Li J, Newman MG, Wang JZ (2020) Arbee: Towards automated recognition of bodily expression of emotion in the wild. Int j comput vis 128(1):1–25

    Google Scholar 

  10. Zhang T, Zheng W, Cui Z, Zong Y, Yan J, Yan K (2016) A deep neural network-driven feature learning method for multi-view facial expression recognition. IEEE Trans Multimed 18(12):2528–2536

    Google Scholar 

  11. Verma B, Choudhary A (2021) Affective state recognition from hand gestures and facial expressions using grassmann manifolds. Multimed Tools Appl 80(9):14019–14040

    Google Scholar 

  12. Verma B, Choudhary A (2018) Deep learning based real-time driver emotion monitoring. In: 2018 IEEE International Conference on Vehicular Electronics and Safety (ICVES), p 1–6. IEEE

  13. Altameem A, Kumar A, Poonia RC, Kumar S, Saudagar AKJ (2021) Early identification and detection of driver drowsiness by hybrid machine learning. IEEE Access 9:162805–162819

    Google Scholar 

  14. Reece AG, Danforth CM (2017) Instagram photos reveal predictive markers of depression. EPJ Data Sci 6(1):15

    Google Scholar 

  15. Manikonda L, De Choudhury M (2017) Modeling and understanding visual attributes of mental health disclosures in social media. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, p 170–181

  16. Kindness P, Masthoff J, Mellish C (2017) Designing emotional support messages tailored to stressors. Int J Hum Comp Studies 97:1–22

    Google Scholar 

  17. Kaliouby Re, Picard R, Baron-Cohen S (2006) Affective computing and autism. Ann N Y Acad Sci 1093(1):228–248

    Google Scholar 

  18. Liu C, Conn K, Sarkar N, Stone W (2008) Physiology-based affect recognition for computer-assisted intervention of children with autism spectrum disorder. Int J Hum Comput Studies 66(9):662–677

    Google Scholar 

  19. Muhammad G, Alsulaiman M, Amin SU, Ghoneim A, Alhamid MF (2017) A facial-expression monitoring system for improved healthcare in smart cities. IEEE Access 5:10871–10881

    Google Scholar 

  20. Uddin MZ, Hassan MM, Almogren A, Alamri A, Alrubaian M, Fortino G (2017) Facial expression recognition utilizing local direction-based robust features and deep belief network. IEEE Access 5:4525–4536

    Google Scholar 

  21. Bishay M, Palasek P, Priebe S, Patras I (2019) Schinet: Automatic estimation of symptoms of schizophrenia from facial behaviour analysis. IEEE Trans Affect Comput

  22. Chen Z, Ansari R, Wilkie D (2019) Learning pain from action unit combinations: a weakly supervised approach via multiple instance learning. IEEE Trans Affect Comput

  23. Lamba PS, Virmani D (2019) Information retrieval from facial expression using voting to assert exigency. J Discrete Math Sci Cryptogr 22(2):177–190

    Google Scholar 

  24. Zheng K, Yang D, Liu J, Cui J (2020) Recognition of teachers’ facial expression intensity based on convolutional neural network and attention mechanism. IEEE Access 8:226437–226444

    Google Scholar 

  25. Ashwin T, Guddeti RMR (2019) Unobtrusive behavioral analysis of students in classroom environment using non-verbal cues. IEEE Access 7:150693–150709

    Google Scholar 

  26. Dawood A, Turner S, Perepa P (2018) Affective computational model to extract natural affective states of students with asperger syndrome (as) in computer-based learning environment. IEEE Access 6:67026–67034

    Google Scholar 

  27. Dampage U, Egodagamage D, Waidyaratne A, Dissanayaka D, Senarathne A (2021) Spatial augmented reality based customer satisfaction enhancement and monitoring system. IEEE Access 9:97990–98004

    Google Scholar 

  28. Aghamaleki JA, Ashkani Chenarlogh V (2019) Multi-stream cnn for facial expression recognition in limited training data. Multimed Tools Appl 78(16):22861–22882

    Google Scholar 

  29. Li BY, Mian AS, Liu W, Krishna A (2013) Using kinect for face recognition under varying poses, expressions, illumination and disguise. In: 2013 IEEE Workshop on Applications of Computer Vision (WACV), p 186–192. IEEE

  30. Saeed U, Masood K, Dawood H (2021) Illumination normalization techniques for makeup-invariant face recognition. Comput Electric Eng 89:106921

    Google Scholar 

  31. Štruc V, Pavesic N (2009) Image normalization techniques for robust face recognition, p 155–160

  32. Li S, Deng W (2020) Deep facial expression recognition: A survey. IEEE trans affect comput

  33. Wen G, Chang T, Li H, Jiang L (2020) Dynamic objectives learning for facial expression recognition. IEEE Trans Multimed 22(11):2914–2925

    Google Scholar 

  34. Pisal A, Sor R, Kinage K (2017) Facial feature extraction using hierarchical max (hmax) method. In: 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), p 1–5. IEEE

  35. Liliana DY, Widyanto MR, BasaruddinT (2018) Geometric facial components feature extraction for facial expression recognition. In: 2018 International Conference on Advanced Computer Science and Information Systems (ICACSIS), p 391–396. IEEE

  36. Boughrara H, Chtourou M, Ben Amar C, Chen L (2016) Facial expression recognition based on a mlp neural network using constructive training algorithm. Multimed Tools Appl 75(2):709–731

    Google Scholar 

  37. El Zarif N, Montazeri L, Leduc-Primeau F, Sawan M (2021) Mobile-optimized facial expression recognition techniques. IEEE Access 9:101172–101185

    Google Scholar 

  38. Pons G, Masip D (2017) Supervised committee of convolutional neural networks in automated facial expression analysis. IEEE Trans Affect Comput 9(3):343–350

    Google Scholar 

  39. Canal FZ, Müller TR, Matias JC, Scotton GG, de Sa Junior AR, Pozzebon E, Sobieranski AC (2022) A survey on facial emotion recognition techniques: A state-of-the-art literature review. Inf Sci 582:593–617

    Google Scholar 

  40. Harms MB, Martin A, Wallace GL (2010) Facial emotion recognition in autism spectrum disorders: a review of behavioral and neuroimaging studies. Neuropsychol Rev 20:290–322

    Google Scholar 

  41. Nelson CA (2001) The development and neural bases of face recognition. Infant Child Develop Int J Res Pract 10(1–2):3–18

    Google Scholar 

  42. Mostafa A, Khalil MI, Abbas H (2018) Emotion recognition by facial features using recurrent neural networks. In: 2018 13th International Conference on Computer Engineering and Systems (ICCES), p 417–422. IEEE

  43. Calvo MG, Fernández-Martín A, Gutiérrez-García A, Lundqvist D (2018) Selective eye fixations on diagnostic face regions of dynamic emotional expressions: Kdef-dyn database. Sci Rep 8(1):1–10

    Google Scholar 

  44. Pantic M, Rothkrantz LJM (2000) Automatic analysis of facial expressions: The state of the art. IEEE TrAns Pattern Anal Mach Intell 22(12):1424–1445

    Google Scholar 

  45. Fukuda T, Jung M-J, Nakashima M, Arai F, Hasegawa Y (2004) Facial expressive robotic head system for human-robot communication and its application in home environment. Proc IEEE 92(11):1851–1865

    Google Scholar 

  46. Ekman P (1993) Facial expression and emotion. Am Psychol 48(4):384

    Google Scholar 

  47. Benitez-Quiroz CF, Srinivasan R, Martinez AM (2018) Discriminant functional learning of color features for the recognition of facial action units and their intensities. IEEE Trans Pattern Anal Mach Intell 41(12):2835–2845

    Google Scholar 

  48. Cimtay Y, Ekmekcioglu E, Caglar-Ozhan S (2020) Cross-subject multimodal emotion recognition based on hybrid fusion. IEEE Access 8:168865–168878

    Google Scholar 

  49. Kollias D, Zafeiriou S (2020) Exploiting multi-cnn features in cnn-rnn based dimensional emotion recognition on the omg in-the-wild dataset. IEEE Trans Affect Comput 12(3):595–606

    Google Scholar 

  50. Liu J, Wang H, Feng Y (2021) An end-to-end deep model with discriminative facial features for facial expression recognition. IEEE Access 9:12158–12166

    Google Scholar 

  51. Zhou N, Liang R, Shi W (2020) A lightweight convolutional neural network for real-time facial expression detection. IEEE Access 9:5573–5584

    Google Scholar 

  52. Fard AP, Mahoor MH (2022) Ad-corre: Adaptive correlation-based loss for facial expression recognition in the wild. IEEE Access 10:26756–26768

    Google Scholar 

  53. Verma B, Choudhary A (2018) A framework for driver emotion recognition using deep learning and grassmann manifolds. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), p 1421–1426. IEEE

  54. Alam M, Vidyaratne LS, Iftekharuddin KM (2018) Sparse simultaneous recurrent deep learning for robust facial expression recognition. IEEE Trans Neural Netw Learn Syst 29(10):4905–4916

    Google Scholar 

  55. Nguyen DH, Kim S, Lee G-S, Yang H-J, Na I-S, Kim SH (2019) Facial expression recognition using a temporal ensemble of multi-level convolutional neural networks. IEEE Trans Affect Comput

  56. Jin X, Lai Z, Jin Z (2021) Learning dynamic relationships for facial expression recognition based on graph convolutional network. IEEE Trans Image Process 30:7143–7155

    Google Scholar 

  57. Perveen N, Roy D, Mohan CK (2018) Spontaneous expression recognition using universal attribute model. IEEE Trans Image Process 27(11):5575–5584

    MathSciNet  Google Scholar 

  58. Miao S, Xu H, Han Z, Zhu Y (2019) Recognizing facial expressions using a shallow convolutional neural network. IEEE Access 7:78000–78011

    Google Scholar 

  59. Xia Z, Hong X, Gao X, Feng X, Zhao G (2019) Spatiotemporal recurrent convolutional networks for recognizing spontaneous micro-expressions. IEEE Trans Multimed 22(3):626–640

    Google Scholar 

  60. Liu Y-J, Li B-J, Lai Y-K (2018) Sparse mdmo: Learning a discriminative feature for micro-expression recognition. IEEE Trans Affect Comput 12(1):254–261

    Google Scholar 

  61. Wang S-J, He Y, Li J, Fu X (2021) Mesnet: A convolutional neural network for spotting multi-scale micro-expression intervals in long videos. IEEE Trans Image Process 30:3956–3969

    Google Scholar 

  62. Hoai M, De la Torre F (2014) Max-margin early event detectors. Int J Comput Vis 107(2):191–202

    MathSciNet  Google Scholar 

  63. Xie L, Tao D, Wei H (2018) Early expression detection via online multi-instance learning with nonlinear extension. IEEE Trans Neural Netw Learn Syst 30(5):1486–1496

    MathSciNet  Google Scholar 

  64. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Google Scholar 

  65. Ruiz-Garcia A, Elshaw M, Altahhan A, Palade V (2018) A hybrid deep learning neural approach for emotion recognition from facial expressions for socially assistive robots. Neural Comput Appl 29(7):359–373

    Google Scholar 

  66. Kulkarni K, Corneanu CA, Ofodile I, Escalera S, Baro X, Hyniewska S, Allik J, Anbarjafari G (2018) Automatic recognition of facial displays of unfelt emotions. IEEE Trans Affect Comput 12(2):377–390

    Google Scholar 

  67. Ab Wahab MN, Nazir A, Ren ATZ, Noor MHM, Akbar MF, Mohamed ASA (2021) Efficientnet-lite and hybrid cnn-knn implementation for facial expression recognition on raspberry pi. IEEE Access 9:134065–134080

    Google Scholar 

  68. Srivastava A (2021) Impact of k-nearest neighbour on classification accuracy in knn algorithm using machine learning. In: Advances in Smart Communication and Imaging Systems, p 363–373. Springer,

  69. Tarnowski P, Kołodziej M, Majkowski A, Rak RJ (2017) Emotion recognition using facial expressions. Proc Comput Sci 108:1175–1184

    Google Scholar 

  70. Salmam FZ, Madani A, Kissi M (2016) Facial expression recognition using decision trees. In: 2016 13th International Conference on Computer Graphics, Imaging and Visualization (CGiV), p 125–130. IEEE

  71. Bailly K, Dubuisson S et al (2017) Dynamic pose-robust facial expression recognition by multi-view pairwise conditional random forests. IEEE Trans Affect Comput 10(2):167–181

    Google Scholar 

  72. Das M, Ghosh SK (2016) Deep-step: A deep learning approach for spatiotemporal prediction of remote sensing data. IEEE Geosci Remote Sens Lett 13(12):1984–1988

    Google Scholar 

  73. Shickel B, Tighe PJ, Bihorac A, Rashidi P (2017) Deep ehr: a survey of recent advances in deep learning techniques for electronic health record (ehr) analysis. IEEE J Biomed Health Inf 22(5):1589–1604

    Google Scholar 

  74. Shao L, Wu D, Li X (2014) Learning deep and wide: A spectral method for learning deep networks. IEEE Trans Neural Netw Learn Syst 25(12):2303–2308

    Google Scholar 

  75. Zhiqiang W, Jun L (2017) A review of object detection based on convolutional neural network. In: 2017 36th Chinese Control Conference (CCC), p 11104–11109. IEEE

  76. Li K, Jin Y, Akram MW, Han R, Chen J (2020) Facial expression recognition with convolutional neural networks via a new face cropping and rotation strategy. Vis Comput 36(2):391–404

    Google Scholar 

  77. Phung VH, Rhee EJ (2019) A high-accuracy model average ensemble of convolutional neural networks for classification of cloud image patches on small datasets. Appl Sci 9(21):4500

    Google Scholar 

  78. Kim J-H, Kim B-G, Roy PP, Jeong D-M (2019) Efficient facial expression recognition algorithm based on hierarchical deep neural network structure. IEEE Access 7:41273–41285

    Google Scholar 

  79. Li M, Xu H, Huang X, Song Z, Liu X, Li X (2018) Facial expression recognition with identity and emotion joint learning. IEEE Trans Affect Comput 12(2):544–550

    Google Scholar 

  80. Fujii K, Sugimura D, Hamamoto T (2020) Hierarchical group-level emotion recognition. IEEE Trans Multimed 23:3892–3906

    Google Scholar 

  81. Zhang H, Jolfaei A, Alazab M (2019) A face emotion recognition method using convolutional neural network and image edge computing. IEEE Access 7:159081–159089

    Google Scholar 

  82. Xie S, Hu H (2017) Facial expression recognition with frr-cnn. Electron Lett 53(4):235–237

    Google Scholar 

  83. Li J, Zhang D, Zhang J, Zhang J, Li T, Xia Y, Yan Q, Xun L (2017) Facial expression recognition with faster r-cnn. Proc Comput Sci 107:135–140

    Google Scholar 

  84. Huang GB, Mattar M, Berg T, Learned-Miller E (2008) Labeled faces in the wild: A database forstudying face recognition in unconstrained environments. In: Workshop on Faces in’Real-Life’Images: Detection, Alignment, and Recognition

  85. Kim J, Kang J-K, Kim Y (2021) A resource efficient integer-arithmetic-only fpga-based cnn accelerator for real-time facial emotion recognition. IEEE Access 9:104367–104381

    Google Scholar 

  86. Zhou Y, Jin L, Liu H, Song E (2020) Color facial expression recognition by quaternion convolutional neural network with gabor attention. IEEE Trans Cogn Develop Syst

  87. Rumelhart G, Hinton R (1986) Williams, learning representations by back-propagating errors. Nature 323:533–536

    Google Scholar 

  88. Jarraya SK, Masmoudi M, Hammami M (2020) Compound emotion recognition of autistic children during meltdown crisis based on deep spatio-temporal analysis of facial geometric features. IEEE Access 8:69311–69326

    Google Scholar 

  89. Wang T, Wen C-K, Wang H, Gao F, Jiang T, Jin S (2017) Deep learning for wireless physical layer: Opportunities and challenges. China Commun 14(11):92–111

    Google Scholar 

  90. Yang B, Cao J, Ni R, Zhang Y (2017) Facial expression recognition using weighted mixture deep neural network based on double-channel facial images. IEEE Access 6:4630–4640

    Google Scholar 

  91. Li T-HS, Kuo P-H, Tsai T-N, Luan P-C (2019) Cnn and lstm based facial expression analysis model for a humanoid robot. IEEE Access 7:93998–94011

    Google Scholar 

  92. Yan S (2016) Understanding lstm and its diagrams. MLReview. com

    Google Scholar 

  93. Dencelin LX, Ramkumar T (2016) Analysis of multilayer perceptron machine learning approach in classifying protein secondary structures. Biomed Res India 27:166–173

    Google Scholar 

  94. Choi DY, Song BC (2020) Facial micro-expression recognition using two-dimensional landmark feature maps. IEEE Access 8:121549–121563

    Google Scholar 

  95. Gong C, Lin F, Zhou X, Lü X (2019) Amygdala-inspired affective computing: To realize personalized intracranial emotions with accurately observed external emotions. China Commun 16(8):115–129

    Google Scholar 

  96. Marques DB, Barradas Filho AO, Romariz AR, Viegas IM, Luz DA, Barros Filho AK, Labidi S, Ferraudo AS (2014) Recent developments on statistical and neural network tools focusing on biodiesel quality. Int J Comput Sci Appl 3(3):97–110

    Google Scholar 

  97. Bhuvaneshwari M, Kanaga EGM, Anitha J, Raimond K, George ST (2021) A comprehensive review on deep learning techniques for a bci-based communication system. Demystifying Big Data Mach Learn Deep Learn Healthcare Anal 131–157

  98. Poux D, Allaert B, Ihaddadene N, Bilasco IM, Djeraba C, Bennamoun M (2021) Dynamic facial expression recognition under partial occlusion with optical flow reconstruction. IEEE Trans Image Process 31:446–457

    Google Scholar 

  99. Fu Y, Wu X, Li X, Pan Z, Luo D (2020) Semantic neighborhood-aware deep facial expression recognition. IEEE Trans Image Process 29:6535–6548

    Google Scholar 

  100. Ng A, Ngiam J, Foo CY, Mai Y, Suen C, Coates A, Maas A, Hannun A, Huval B, Wang T et al (2015) Deep learning tutorial. Univ, Stanford

    Google Scholar 

  101. Zhao Z-j, Gu J-w (2015) Recognition of digital modulation signals based on hybrid three-order restricted boltzmann machine. In: 2015 IEEE 16th International Conference on Communication Technology (ICCT), p 166–169. IEEE

  102. Wang Y, Li Y, Song Y, Rong X (2019) The application of a hybrid transfer algorithm based on a convolutional neural network model and an improved convolution restricted boltzmann machine model in facial expression recognition. IEEE Access 7:184599–184610

    Google Scholar 

  103. Lin R, Yang F, Gao M, Wu B, Zhao Y (2019) Aud-mts: An abnormal user detection approach based on power load multi-step clustering with multiple time scales. Energies 12(16):3144

    Google Scholar 

  104. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. Adv neural inf process syst 27

  105. Yan Y, Huang Y, Chen S, Shen C, Wang H (2019) Joint deep learning of facial expression synthesis and recognition. IEEE Trans Multimed 22(11):2792–2807

    Google Scholar 

  106. Li C, Wang Y, Zhang X, Gao H, Yang Y, Wang J (2019) Deep belief network for spectral-spatial classification of hyperspectral remote sensor data. Sensors 19(1):204

    Google Scholar 

  107. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J, Houlsby N (2021) An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations. https://openreview.net/forum?id=YicbFdNTTy

  108. Ma F, Sun B, Li S (2021) Facial expression recognition with visual transformers and attentional selective fusion. IEEE Trans Affect Comput

  109. Xue F, Wang Q, Guo G (2021) Transfer: Learning relation-aware facial expression representations with transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, p 3601–3610

  110. Huang Q, Huang C, Wang X, Jiang F (2021) Facial expression recognition with grid-wise attention and visual transformer. Inf Sci 580:35–54

    MathSciNet  Google Scholar 

  111. Liu C, Hirota K, Dai Y (2023) Patch attention convolutional vision transformer for facial expression recognition with occlusion. Inf Sci 619:781–794

    Google Scholar 

  112. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Advances in neural information processing systems 30

  113. Liu X, Cheng X, Lee K (2020) Ga-svm-based facial emotion recognition using facial geometric features. IEEE Sensors J 21(10):11532–11542

    Google Scholar 

  114. Zhen Q, Huang D, Drira H, Amor BB, Wang Y, Daoudi M (2017) Magnifying subtle facial motions for effective 4d expression recognition. IEEE Trans Affect Comput 10(4):524–536

    Google Scholar 

  115. Xu F, Zhang J, Wang JZ (2017) Microexpression identification and categorization using a facial dynamics map. IEEE Trans Affect Comput 8(2):254–267

    Google Scholar 

  116. Zhao G, Yang H, Yu M (2020) Expression recognition method based on a lightweight convolutional neural network. IEEE Access 8:38528–38537

    Google Scholar 

  117. Li Y, Zeng J, Shan S, Chen X (2018) Occlusion aware facial expression recognition using cnn with attention mechanism. IEEE Trans Image Process 28(5):2439–2450

    MathSciNet  Google Scholar 

  118. Liu C, Hirota K, Ma J, Jia Z, Dai Y (2021) Facial expression recognition using hybrid features of pixel and geometry. IEEE Access 9:18876–18889

    Google Scholar 

  119. Zhang H, Su W, Yu J, Wang Z (2020) Identity-expression dual branch network for facial expression recognition. IEEE Trans Cogn Develop Syst 13(4):898–911

    Google Scholar 

  120. Zhang H, Su W, Wang Z (2020) Weakly supervised local-global attention network for facial expression recognition. IEEE Access 8:37976–37987

    Google Scholar 

  121. Kabakus AT (2020) Pyfer: A facial expression recognizer based on convolutional neural networks. IEEE Access 8:142243–142249

    Google Scholar 

  122. Song B, Li K, Zong Y, Zhu J, Zheng W, Shi J, Zhao L (2019) Recognizing spontaneous micro-expression using a three-stream convolutional neural network. IEEE Access 7:184537–184551

    Google Scholar 

  123. Xie S, Hu H (2018) Facial expression recognition using hierarchical features with deep comprehensive multipatches aggregation convolutional neural networks. IEEE Trans Multimed 21(1):211–220

    Google Scholar 

  124. Salih WM, Nadher I, Tariq A (2019) Deep learning for face expressions detection: Enhanced recurrent neural network with long short term memory. In: International Conference on Applied Computing to Support Industry: Innovation and Technology, p 237–247. Springer

  125. Hong Q-B, Wu C-H, Su M-H, Chang C-C (2019) Exploring macroscopic and microscopic fluctuations of elicited facial expressions for mood disorder classification. IEEE Trans Affect Comput 12(4):989–1001

    Google Scholar 

  126. Barman A, Dutta P (2021) Facial expression recognition using distance and shape signature features. Pattern Recognit Lett 145:254–261

    Google Scholar 

  127. Nie S, Wang Z, Ji Q (2015) A generative restricted boltzmann machine based method for high-dimensional motion data modeling. Comput Vis Image Underst 136:14–22

    Google Scholar 

  128. Li D, Li Z, Luo R, Deng J, Sun S (2019) Multi-pose facial expression recognition based on generative adversarial network. IEEE Access 7:143980–143989

    Google Scholar 

  129. Kim S, Nam J, Ko BC (2022) Facial expression recognition based on squeeze vision transformer. Sensors 22(10):3729

    Google Scholar 

  130. Cohn JF, Zlochower AJ, Lien J, Kanade T (1999) Automated face analysis by feature point tracking has high concurrent validity with manual facs coding. Psychophysiology 36(1):35–43

    Google Scholar 

  131. Zeng G, Zhou J, Jia X, Xie W, Shen L (2018) Hand-crafted feature guided deep learning for facial expression recognition. In: 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), p 423–430. IEEE

  132. Kumar S, Bhuyan MK, Chakraborty BK (2016) Extraction of informative regions of a face for facial expression recognition. IET Comput Vis 10(6):567–576

    Google Scholar 

  133. Kurup AR, Ajith M, Ramón MM (2019) Semi-supervised facial expression recognition using reduced spatial features and deep belief networks. Neurocomputing 367:188–197

    Google Scholar 

  134. Jain DK, Shamsolmoali P, Sehdev P (2019) Extended deep neural network for facial emotion recognition. Pattern Recognit Lett 120:69–74

    Google Scholar 

  135. Sen D, Datta S, Balasubramanian R (2019) Facial emotion classification using concatenated geometric and textural features. Multimed Tools Appl 78(8):10287–10323

    Google Scholar 

  136. Fei Z, Yang E, Li DD-U, Butler S, Ijomah W, Li X, Zhou H (2020) Deep convolution network based emotion analysis towards mental health care. Neurocomputing 388:212–227

    Google Scholar 

  137. Shahid AR, Khan S, Yan H (2020) Contour and region harmonic features for sub-local facial expression recognition. J Vis Commun Image Represent 73:102949

    Google Scholar 

  138. Chowdary MK, Nguyen TN, Hemanth DJ (2021) Deep learning-based facial emotion recognition for human-computer interaction applications. Neural Comput Appl 1–18

  139. Saurav S, Saini R, Singh S (2021) Facial expression recognition using dynamic local ternary patterns with kernel extreme learning machine classifier. IEEE Access 9:120844–120868

    Google Scholar 

  140. Liu J, Feng Y, Wang H (2021) Facial expression recognition using pose-guided face alignment and discriminative features based on deep learning. IEEE Access 9:69267–69277

    Google Scholar 

  141. Niu B, Gao Z, Guo B (2021) Facial expression recognition with lbp and orb features. Comput Intell Neurosci 2021

  142. Kas M, Ruichek Y, Messoussi R et al (2021) New framework for person-independent facial expression recognition combining textural and shape analysis through new feature extraction approach. Inf Sci 549:200–220

    MathSciNet  Google Scholar 

  143. Shao J, Cheng Q (2021) E-fcnn for tiny facial expression recognition. Appl Intell 51:549–559

    Google Scholar 

  144. Wan F, Zhi R (2022) Gaussian distribution-based facial expression feature extraction network. Pattern Recognit Lett 164:104–111

    Google Scholar 

  145. Ahadit AB, Jatoth RK (2022) A novel multi-feature fusion deep neural network using hog and vgg-face for facial expression classification. Mach Vis Appl 33(4):1–23

    Google Scholar 

  146. Dar T, Javed A, Bourouis S, Hussein HS, Alshazly H (2022) Efficient-swishnet based system for facial emotion recognition. IEEE Access 10:71311–71328

    Google Scholar 

  147. Umer S, Rout RK, Pero C, Nappi M (2022) Facial expression recognition with trade-offs between data augmentation and deep learning features. J Ambient Intell Humaniz Comput 13(2):721–735

    Google Scholar 

  148. Yang J, Lv Z, Kuang K, Yang S, Xiao L, Tang Q (2022) Rasn: Using attention and sharing affinity features to address sample imbalance in facial expression recognition. IEEE Access 10:103264–103274

    Google Scholar 

  149. Bentoumi M, Daoud M, Benaouali M, Taleb Ahmed A (2022) Improvement of emotion recognition from facial images using deep learning and early stopping cross validation. Multimed Tools appl 1–31

  150. Zou W, Zhang D, Lee D-J (2022) A new multi-feature fusion based convolutional neural network for facial expression recognition. Appl Intell 52(3):2918–2929

    Google Scholar 

  151. Aifanti N, Papachristou C, Delopoulos A (2010) The mug facial expression database. In: 11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10, p 1–4. IEEE

  152. Gopalan N, Bellamkonda S, Chaitanya VS (2018) Facial expression recognition using geometric landmark points and convolutional neural networks. In: 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), p 1149–1153. IEEE

  153. Barman A, Dutta P (2018) Facial expression recognition using distance signature feature. In: Advanced Computational and Communication Paradigms: Proceedings of International Conference on ICACCP 2017, Volume 2, p 155–163. Springer

  154. Verma M, Vipparthi SK, Singh G (2019) Hinet: Hybrid inherited feature learning network for facial expression recognition. IEEE Lett Comput Soc 2(4):36–39

    Google Scholar 

  155. Barman A, Dutta P (2019) Influence of shape and texture features on facial expression recognition. IET Image Process 13(8):1349–1363

    Google Scholar 

  156. Barman A, Dutta P (2019) Facial expression recognition using distance and texture signature relevant features. Appl Soft Comput 77:88–105

    Google Scholar 

  157. Dirik M (2022) Optimized anfis model with hybrid metaheuristic algorithms for facial emotion recognition. Int J Fuzzy Syst 1–12

  158. Lyons, M., Akamatsu, S., Kamachi, M., Gyoba, J (1998) Coding facial expressions with gabor wavelets. In: Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition, p 200–205. IEEE

  159. Dubey AK, Jain V (2020) Automatic facial recognition using vgg16 based transfer learning model. J Inf Optim Sci 41(7):1589–1596

    Google Scholar 

  160. Minaee S, Minaei M, Abdolrashidi A (2021) Deep-emotion: Facial expression recognition using attentional convolutional network. Sensors 21(9):3046

    Google Scholar 

  161. Kola DGR, Samayamantula SK (2021) A novel approach for facial expression recognition using local binary pattern with adaptive window. Multimed Tools Appl 80(2):2243–2262

    Google Scholar 

  162. Mahesh VG, Chen C, Rajangam V, Raj ANJ, Krishnan PT (2021) Shape and texture aware facial expression recognition using spatial pyramid zernike moments and law’s textures feature set. IEEE Access 9:52509–52522

    Google Scholar 

  163. Barros P, Sciutti A (2022) Across the universe: Biasing facial representations toward non-universal emotions with the face-stn. IEEE Access 10:103932–103947

    Google Scholar 

  164. Tsalera E, Papadakis A, Samarakou M, Voyiatzis I (2022) Feature extraction with handcrafted methods and convolutional neural networks for facial emotion recognition. Appl Sci 12(17):8455

    Google Scholar 

  165. Su C, Wei J, Lin D, Kong L (2022) Using attention lsgb network for facial expression recognition. Pattern Anal Appl 1–11

  166. Lundqvist D, Flykt A, Öhman A (1998) Karolinska directed emotional faces. Cogn Emot

  167. Pandey RK, Karmakar S, Ramakrishnan A, Saha N (2019) Improving facial emotion recognition systems with crucial feature extractors. In: Image Analysis and Processing–ICIAP 2019: 20th International Conference, Trento, Italy, September 9–13, 2019, Proceedings, Part I 20, p 268–279. Springer

  168. Fei Z, Yang E, Li D, Butler S, Ijomah W, Zhou H (2019) Combining deep neural network with traditional classifier to recognize facial expressions. In: 2019 25th International Conference on Automation and Computing (ICAC), p 1–6. IEEE

  169. Akhand M, Roy S, Siddique N, Kamal MAS, Shimamura T (2021) Facial emotion recognition using transfer learning in the deep cnn. Electronics 10(9):1036

    Google Scholar 

  170. Cho S, Lee J (2022) Learning local attention with guidance map for pose robust facial expression recognition. IEEE Access 10:85929–85940

    Google Scholar 

  171. Goodfellow IJ, Erhan D, Carrier PL, Courville A, Mirza M, Hamner B, Cukierski W, Tang Y, Thaler D, Lee D-H, et al (2013) Challenges in representation learning: A report on three machine learning contests. In: International Conference on Neural Information Processing, p 117–124. Springer

  172. Zhang X, Ma Y (2018) Improving ensemble learning performance with complementary neural networks for facial expression recognition. In: International Conference on Artificial Neural Networks, p 747–759. Springer

  173. Vorontsov A, Averkin A (2018) Comparison of different convolution neural network architectures for the solution of the problem of emotion recognition by facial expression. In: Proceedings of the VIII International Conference “Distributed Computing and Grid-technologies in Science and Education” (GRID 2018), Dubna, Moscow Region, Russia, p 35–40

  174. Ramdhani B, Djamal EC, Ilyas R (2018) Convolutional neural networks models for facial expression recognition. In: 2018 International Symposium on Advanced Intelligent Informatics (SAIN), p 96–101. IEEE

  175. Hua W, Dai F, Huang L, Xiong J, Gui G (2019) Hero: Human emotions recognition for realizing intelligent internet of things. IEEE Access 7:24321–24332

    Google Scholar 

  176. Georgescu M-I, Ionescu RT, Popescu M (2019) Local learning with deep and handcrafted features for facial expression recognition. IEEE Access 7:64827–64836

    Google Scholar 

  177. Talegaonkar I, Joshi K, Valunj S, Kohok R, Kulkarni A (2019) Real time facial expression recognition using deep learning. In: Proceedings of International Conference on Communication and Information Processing (ICCIP)

  178. Jiang P, Wan B, Wang Q, Wu J (2020) Fast and efficient facial expression recognition using a gabor convolutional network. IEEE Signal Process Lett 27:1954–1958

    Google Scholar 

  179. Kim JH, Poulose A, Han DS (2021) The extensive usage of the facial image threshing machine for facial emotion recognition performance. Sensors 21(6):2026

    Google Scholar 

  180. Liang X, Xu L, Zhang W, Zhang Y, Liu J, Liu Z (2022) A convolution-transformer dual branch network for head-pose and occlusion facial expression recognition. Vis Comput 1–14

  181. Yen C-T, Li K-H (2022) Discussions of different deep transfer learning models for emotion recognitions. IEEE Access 10:102860–102875

    Google Scholar 

  182. Liu H, Cai H, Lin Q, Li X, Xiao H (2022) Adaptive multilayer perceptual attention network for facial expression recognition. IEEE Trans Circuits Syst Vid Technol 32(9):6253–6266

    Google Scholar 

  183. Lu Y, Wang S, Zhao W, Zhao Y (2019) Wgan-based robust occluded facial expression recognition. IEEE Access 7:93594–93610

    Google Scholar 

  184. Barsoum E, Zhang C, Ferrer CC, Zhang Z (2016) Training deep networks for facial expression recognition with crowd-sourced label distribution. In: Proceedings of the 18th ACM International Conference on Multimodal Interaction, p 279–283

  185. Albanie S, Nagrani A, Vedaldi A, Zisserman A (2018) Emotion recognition in speech using cross-modal transfer in the wild. In: Proceedings of the 26th ACM International Conference on Multimedia, p 292–301

  186. Wang K, Peng X, Yang J, Meng D, Qiao Y (2020) Region attention networks for pose and occlusion robust facial expression recognition. IEEE Trans Image Process 29:4057–4069

    Google Scholar 

  187. Wang K, Peng X, Yang J, Lu S, Qiao Y (2020) Suppressing uncertainties for large-scale facial expression recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, p 6897–6906

  188. Li H, Wang N, Ding X, Yang X, Gao X (2021) Adaptively learning facial expression representation via cf labels and distillation. IEEE Trans Image Process 30:2016–2028

    Google Scholar 

  189. Li S, Deng W, Du J (2017) Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, p 2852–2861

  190. Wang Z, Zeng F, Liu S, Zeng B (2021) Oaenet: Oriented attention ensemble for accurate facial expression recognition. Pattern Recognit 112:107694

    Google Scholar 

  191. Cao S, Yao Y, An G (2020) E2-capsule neural networks for facial expression recognition using au-aware attention. IET Image Process 14(11):2417–2424

    Google Scholar 

  192. Zhao Z, Liu Q, Wang S (2021) Learning deep global multi-scale and local attention features for facial expression recognition in the wild. IEEE Trans Image Process 30:6544–6556

    Google Scholar 

  193. Farzaneh AH, Qi X (2021) Facial expression recognition in the wild via deep attentive center loss. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, p 2402–2411

  194. Saurav S, Saini R, Singh S (2021) Emnet: a deep integrated convolutional neural network for facial emotion recognition in the wild. Appl Intell 51:5543–5570

    Google Scholar 

  195. Yan W-J, Li X, Wang S-J, Zhao G, Liu Y-J, Chen Y-H, Fu X (2014) Casme ii: An improved spontaneous micro-expression database and the baseline evaluation. PloS One 9(1):86041

    Google Scholar 

  196. Liong S-T, See J, Wong K, Phan RC-W (2018) Less is more: Micro-expression recognition from video using apex frame. Signal Process Image Commun 62:82–92

    Google Scholar 

  197. Zong Y, Huang X, Zheng W, Cui Z, Zhao G (2018) Learning from hierarchical spatiotemporal descriptors for micro-expression recognition. IEEE Trans Multimed 20(11):3160–3172

    Google Scholar 

  198. Zhi R, Xu H, Wan M, Li T (2019) Combining 3d convolutional neural networks with transfer learning by supervised pre-training for facial micro-expression recognition. IEICE Trans Inf Syst 102(5):1054–1064

    Google Scholar 

  199. Verma M, Vipparthi SK, Singh G, Murala S (2019) Learnet: Dynamic imaging network for micro expression recognition. IEEE Trans Image Process 29:618–1627

    MathSciNet  Google Scholar 

  200. Li Y, Huang X, Zhao G (2020) Joint local and global information learning with single apex frame detection for micro-expression recognition. IEEE Trans Image Process 30:249–263

    Google Scholar 

  201. Xie H-X, Lo L, Shuai H-H, Cheng W-H (2020) Au-assisted graph attention convolutional network for micro-expression recognition. In: Proceedings of the 28th ACM International Conference on Multimedia, p 2871–2880

  202. Cen S, Yu Y, Yan G, Yu M, Yang Q (2020) Sparse spatiotemporal descriptor for micro-expression recognition using enhanced local cube binary pattern. Sensors 20(16):4437

    Google Scholar 

  203. Wang C, Peng M, Bi T, Chen T (2020) Micro-attention for micro-expression recognition. Neurocomputing 410:354–362

    Google Scholar 

  204. Kumar AJR, Bhanu B (2021) Micro-expression classification based on landmark relations with graph attention convolutional network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, p 1511–1520

  205. Saeed U (2021) Facial micro-expressions as a soft biometric for person recognition. Pattern Recognit Lett 143:95–103

    Google Scholar 

  206. Wei J, Lu G, Yan J (2021) A comparative study on movement feature in different directions for micro-expression recognition. Neurocomputing 449:159–171

    Google Scholar 

  207. Liu K-H, Jin Q-S, Xu H-C, Gan Y-S, Liong S-T (2021) Micro-expression recognition using advanced genetic algorithm. Signal Process Image Commun 93:116153

    Google Scholar 

  208. Nie X, Takalkar MA, Duan M, Zhang H, Xu M (2021) Geme: Dual-stream multi-task gender-based micro-expression recognition. Neurocomputing 427:13–28

    Google Scholar 

  209. Cai L, Li H, Dong W, Fang H (2022) Micro-expression recognition using 3d densenet fused squeeze-and-excitation networks. Appl Soft Comput 119:108594

    Google Scholar 

  210. Wei J, Lu G, Yan J, Liu H (2022) Micro-expression recognition using local binary pattern from five intersecting planes. Multimed Tools Appl 1–26

  211. Liu S, Ren Y, Li L, Sun X, Song Y, Hung C-C (2022) Micro-expression recognition based on squeezenet and c3d. Multimed Syst 1–10

  212. Langner O, Dotsch R, Bijlstra G, Wigboldus DH, Hawk ST, Van Knippenberg A (2010) Presentation and validation of the radboud faces database. Cogn Emot 24(8):1377–1388

    Google Scholar 

  213. González-Hernández F, Zatarain-Cabada R, Barrón-Estrada ML, Rodríguez-Rangel H (2018) Recognition of learning-centered emotions using a convolutional neural network. J Intell Fuzzy Syst 34(5):3325–3336

    Google Scholar 

  214. Sun N, Li Q, Huan R, Liu J, Han G (2019) Deep spatial-temporal feature fusion for facial expression recognition in static images. Pattern Recognit Lett 119:49–61

    Google Scholar 

  215. Yolcu G, Oztel I, Kazan S, Oz C, Palaniappan K, Lever TE, Bunyak F (2019) Facial expression recognition for monitoring neurological disorders based on convolutional neural network. Multimed Tools Appl 78(22):31581–31603

    Google Scholar 

  216. Sun N, Lu Q, Zheng W, Liu J, Han G (2020) Unsupervised cross-view facial expression image generation and recognition. IEEE Trans Affect Comput

  217. He J, Yu X, Sun B, Yu L (2021) Facial expression and action unit recognition augmented by their dependencies on graph convolutional networks. J Multimodal User Interfaces 15(4):429–440

    Google Scholar 

  218. Fan X, Jiang M, Shahid AR, Yan H (2022) Hierarchical scale convolutional neural network for facial expression recognition. Cogn Neurodynamics 1–12

  219. Mollahosseini A, Hasani B, Mahoor MH (2017) Affectnet: A database for facial expression, valence, and arousal computing in the wild. IEEE Trans Affect Comput 10(1):18–31

    Google Scholar 

  220. Hung SC, Lee J-H, Wan TS, Chen C-H, Chan Y-M, Chen C-S (2019) Increasingly packing multiple facial-informatics modules in a unified deep-learning model via lifelong learning. In: Proceedings of the 2019 on International Conference on Multimedia Retrieval, p 339–343

  221. Vo T-H, Lee G-S, Yang H-J, Kim S-H (2020) Pyramid with super resolution for in-the-wild facial expression recognition. IEEE Access 8:131988–132001

    Google Scholar 

  222. Schoneveld L, Othmani A, Abdelkawy H (2021) Leveraging recent advances in deep learning for audio-visual emotion recognition. Pattern Recognit Lett 146:1–7

    Google Scholar 

  223. Davison AK, Lansley C, Costen N, Tan K, Yap MH (2016) Samm: A spontaneous micro-facial movement dataset. IEEE Trans Affect Comput 9(1):116–129

    Google Scholar 

  224. Khor H-Q, See J, Liong S-T, Phan RC, Lin W (2019) Dual-stream shallow networks for facial micro-expression recognition. In: 2019 IEEE International Conference on Image Processing (ICIP), p 36–40. IEEE

  225. Lei L, Li J, Chen T, Li S (2020) A novel graph-tcn with a graph structured representation for micro-expression recognition. In: Proceedings of the 28th ACM International Conference on Multimedia, p 2237–2245

  226. Pfister T, Li X, Zhao G, Pietikäinen M (2011) Recognising spontaneous facial micro-expressions. In: 2011 International Conference on Computer Vision, p 1449–1456. IEEE

  227. Zhang Z, Yi M, Xu J, Zhang R, Shen J (2020) Two-stage recognition and beyond for compound facial emotion recognition. In: 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), p 900–904. IEEE

  228. Dai Y, Feng L (2021) Cross-domain few-shot micro-expression recognition incorporating action units. IEEE Access 9:142071–142083

    MathSciNet  Google Scholar 

  229. Ali G, Ali A, Ali F, Draz U, Majeed F, Yasin S, Ali T, Haider N (2020) Artificial neural network based ensemble approach for multicultural facial expressions analysis. IEEE Access 8:134950–134963

    Google Scholar 

  230. Ye Y, Pan Y, Liang Y, Pan J (2023) A cascaded spatiotemporal attention network for dynamic facial expression recognition. Appl Intell 53(5):5402–5415

    Google Scholar 

Download references

Acknowledgements

The author would like to acknowledge the Department of Information Technology, Delhi Technological University, New Delhi, India for providing me necessary resources to carry out the research.

Funding

We received no funding.

Author information

Authors and Affiliations

  1. Department of Information Technology, Delhi Technological University, New Delhi, 110042, Delhi, India

    Nidhi & Bindu Verma

Authors
  1. Nidhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bindu Verma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bindu Verma.

Ethics declarations

Conflicts of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nidhi, Verma, B. From methods to datasets: a detailed study on facial emotion recognition. Appl Intell 53, 30219–30249 (2023). https://doi.org/10.1007/s10489-023-05052-y

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-023-05052-y

Keywords

Search

Navigation