{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T16:36:55Z","timestamp":1776875815260,"version":"3.51.2"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T00:00:00Z","timestamp":1679529600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T00:00:00Z","timestamp":1679529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-023-01727-y","type":"journal-article","created":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T15:02:41Z","timestamp":1679583761000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Facial Landmark-Based Human Emotion Recognition Technique for Oriented Viewpoints in the Presence of Facial Attributes"],"prefix":"10.1007","volume":"4","author":[{"given":"Utkarsh","family":"Sharma","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazi Newaj","family":"Faisal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6835-003X","authenticated-orcid":false,"given":"Rishi Raj","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"K. V.","family":"Arya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"issue":"3","key":"1727_CR1","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1007\/s00371-019-01628-3","volume":"36","author":"A Joseph","year":"2020","unstructured":"Joseph A, Geetha P. Facial emotion detection using modified eyemap-mouthmap algorithm on an enhanced image and classification with tensorflow. Visual Comput. 2020;36(3):529\u201339. https:\/\/doi.org\/10.1007\/s00371-019-01628-3.","journal-title":"Visual Comput"},{"issue":"4","key":"1727_CR2","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1016\/j.neunet.2005.03.006","volume":"18","author":"N Fragopanagos","year":"2005","unstructured":"Fragopanagos N, Taylor JG. Emotion recognition in human-computer interaction. Neural Networks. 2005;18(4):389\u2013405.","journal-title":"Neural Networks"},{"issue":"5","key":"1727_CR3","doi-asserted-by":"publisher","first-page":"1389","DOI":"10.1007\/s11554-019-00896-5","volume":"17","author":"L Greche","year":"2020","unstructured":"Greche L, Akil M, Kachouri R, Es-sbai N. A new pipeline for the recognition of universal expressions of multiple faces in a video sequence. J Real Time Image Process. 2020;17(5):1389\u2013402.","journal-title":"J Real Time Image Process"},{"key":"1727_CR4","doi-asserted-by":"crossref","unstructured":"Iyer A, Das SS, Teotia R, Maheshwari S, Sharma RR. CNN and LSTM based ensemble learning for human emotion recognition using EEG recordings. Multimedia Tools Appl. 2022.","DOI":"10.1007\/s11042-022-12310-7"},{"key":"1727_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.dsp.2021.103153","volume":"117","author":"T Chen","year":"2021","unstructured":"Chen T, Yin H, Yuan X, Gu Y, Ren F, Sun X. Emotion recognition based on fusion of long short-term memory networks and SVMs. Digital Signal Process. 2021;117: 103153.","journal-title":"Digital Signal Process"},{"key":"1727_CR6","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1016\/j.neucom.2015.05.037","volume":"168","author":"Y Chen","year":"2015","unstructured":"Chen Y, Yang Z, Wang J. Eyebrow emotional expression recognition using surface EMG signals. Neurocomputing. 2015;168:871\u20139.","journal-title":"Neurocomputing"},{"issue":"1","key":"1727_CR7","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1109\/TAFFC.2015.2392101","volume":"6","author":"K Wang","year":"2015","unstructured":"Wang K, An N, Li BN, Zhang Y, Li L. Speech emotion recognition using Fourier parameters. IEEE Trans Affect Comput. 2015;6(1):69\u201375.","journal-title":"IEEE Trans Affect Comput"},{"issue":"1","key":"1727_CR8","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12839","volume":"39","author":"C Pabba","year":"2022","unstructured":"Pabba C, Kumar P. An intelligent system for monitoring students\u2019 engagement in large classroom teaching through facial expression recognition. Expert Syst. 2022;39(1): e12839.","journal-title":"Expert Syst"},{"issue":"5","key":"1727_CR9","doi-asserted-by":"publisher","first-page":"3085","DOI":"10.3390\/ijerph19053085","volume":"19","author":"SB Sukhavasi","year":"2022","unstructured":"Sukhavasi SB, Sukhavasi SB, Elleithy K, El-Sayed A, Elleithy A. A hybrid model for driver emotion detection using feature fusion approach. Int J Environ Res Public Health. 2022;19(5):3085.","journal-title":"Int J Environ Res Public Health"},{"issue":"8","key":"1727_CR10","volume":"34","author":"N Samadiani","year":"2022","unstructured":"Samadiani N, Huang G, Luo W, Chi CH, Shu Y, Wang R, Kocaturk T. A multiple feature fusion framework for video emotion recognition in the wild. Concurren Comput. 2022;34(8): e5764.","journal-title":"Concurren Comput"},{"key":"1727_CR11","doi-asserted-by":"crossref","unstructured":"Savin AV, Sablina VA, Nikiforov MB. Comparison of facial landmark detection methods for micro-expressions analysis. In: 2021 10th Mediterranean Conference on Embedded Computing (MECO), IEEE. 2021;pp. 1\u20134.","DOI":"10.1109\/MECO52532.2021.9460191"},{"key":"1727_CR12","doi-asserted-by":"crossref","unstructured":"Siam AI, Soliman NF, Algarni AD, El-Samie A, Fathi E, Sedik A. Deploying machine learning techniques for human emotion detection. Comput Intel Neurosci. 2022.","DOI":"10.1155\/2022\/8032673"},{"key":"1727_CR13","doi-asserted-by":"crossref","unstructured":"Gomez LF, Morales A, Orozco-Arroyave JR, Daza R, Fierrez J. Improving parkinson detection using dynamic features from evoked expressions in video. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2021;pp. 1562\u20131570.","DOI":"10.1109\/CVPRW53098.2021.00172"},{"key":"1727_CR14","doi-asserted-by":"crossref","unstructured":"Rao Q, Qu X, Mao Q, Zhan Y. Multi-pose facial expression recognition based on surf boosting. In: 2015 international conference on affective computing and intelligent interaction (ACII). IEEE 2015;pp. 630\u2013635.","DOI":"10.1109\/ACII.2015.7344635"},{"issue":"3","key":"1727_CR15","doi-asserted-by":"publisher","first-page":"1282","DOI":"10.1016\/j.patcog.2013.10.010","volume":"47","author":"A Majumder","year":"2014","unstructured":"Majumder A, Behera L, Subramanian VK. Emotion recognition from geometric facial features using self-organizing map. Pattern Recogn. 2014;47(3):1282\u201393.","journal-title":"Pattern Recogn"},{"key":"1727_CR16","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.patrec.2017.10.022","volume":"119","author":"N Sun","year":"2019","unstructured":"Sun N, Li Q, Huan R, Liu J, Han G. Deep spatial-temporal feature fusion for facial expression recognition in static images. Pattern Recogn Lett. 2019;119:49\u201361.","journal-title":"Pattern Recogn Lett"},{"issue":"6","key":"1727_CR17","doi-asserted-by":"publisher","first-page":"1357","DOI":"10.1109\/TPAMI.2012.233","volume":"35","author":"O Rudovic","year":"2012","unstructured":"Rudovic O, Pantic M, Patras I. Coupled Gaussian processes for pose-invariant facial expression recognition. IEEE Trans Pattern Anal Mach Intel. 2012;35(6):1357\u201369.","journal-title":"IEEE Trans Pattern Anal Mach Intel"},{"issue":"12","key":"1727_CR18","doi-asserted-by":"publisher","first-page":"2528","DOI":"10.1109\/TMM.2016.2598092","volume":"18","author":"T Zhang","year":"2016","unstructured":"Zhang T, Zheng W, Cui Z, Zong Y, Yan J, Yan K. A deep neural network-driven feature learning method for multi-view facial expression recognition. IEEE Trans Multimedia. 2016;18(12):2528\u201336.","journal-title":"IEEE Trans Multimedia"},{"key":"1727_CR19","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.patrec.2022.01.013","volume":"155","author":"D Gera","year":"2022","unstructured":"Gera D, Balasubramanian S, Jami A. Cern: Compact facial expression recognition net. Pattern Recogn Lett. 2022;155:9\u201318.","journal-title":"Pattern Recogn Lett"},{"key":"1727_CR20","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.patrec.2021.04.030","volume":"148","author":"W Hariri","year":"2021","unstructured":"Hariri W, Farah N. Recognition of 3D emotional facial expression based on handcrafted and deep feature combination. Pattern Recogn Lett. 2021;148:84\u201391.","journal-title":"Pattern Recogn Lett"},{"issue":"8","key":"1727_CR21","doi-asserted-by":"publisher","first-page":"1377","DOI":"10.1080\/02699930903485076","volume":"24","author":"O Langner","year":"2010","unstructured":"Langner O, Dotsch R, Bijlstra G, Wigboldus DH, Hawk ST, Van Knippenberg A. Presentation and validation of the radboud faces database. Cogn Emot. 2010;24(8):1377\u201388.","journal-title":"Cogn Emot"},{"key":"1727_CR22","unstructured":"Lyons M, Kamachi M, Gyoba J.The Japanese Female Facial Expression (JAFFE) Dataset 1998. 10.5281\/zenodo.3451524"},{"issue":"9","key":"1727_CR23","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1016\/j.imavis.2011.07.002","volume":"29","author":"G Zhao","year":"2011","unstructured":"Zhao G, Huang X, Taini M, Li SZ, Pietik\u00e4Inen M. Facial expression recognition from near-infrared videos. Image Vis Comput. 2011;29(9):607\u201319.","journal-title":"Image Vis Comput"},{"key":"1727_CR24","unstructured":"Kanade T, Cohn JF, Tian Y.Comprehensive database for facial expression analysis. In: Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580. IEEE 2000;pp. 46\u201353"},{"key":"1727_CR25","doi-asserted-by":"crossref","unstructured":"Lucey P, Cohn JF, Kanade T, Saragih J, Ambadar Z, Matthews I.The extended cohn-kanade dataset (ck+): A complete dataset for action unit and emotion-specified expression. In: 2010 ieee computer society conference on computer vision and pattern recognition-workshops. IEEE. 2010;pp. 94\u2013101.","DOI":"10.1109\/CVPRW.2010.5543262"},{"key":"1727_CR26","unstructured":"Yin L, Wei X, Sun Y, Wang J, Rosato MJ. A 3d facial expression database for facial behavior research. In: 7th international conference on automatic face and gesture recognition (FGR06). IEEE 2006;pp. 211\u2013216."},{"key":"1727_CR27","unstructured":"Valstar M, Pantic M, et\u00a0al. Induced disgust, happiness and surprise: an addition to the mmi facial expression database. In: Proc. 3rd Intern. Workshop on EMOTION (satellite of LREC): Corpora for Research on Emotion and Affect. Paris, France. 2010;p.\u00a065."},{"key":"1727_CR28","doi-asserted-by":"crossref","unstructured":"Lundqvist D, Flykt A, \u00d6hman A. Karolinska directed emotional faces. Cognition and Emotion. 1998.","DOI":"10.1037\/t27732-000"},{"key":"1727_CR29","doi-asserted-by":"crossref","unstructured":"Arya KVS, Gupta RK, Agarwal S, Gupta P. IIITM Face: A database for facial attribute detection in constrained and simulated unconstrained environments. In: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD. 2020;pp. 185\u2013189.","DOI":"10.1145\/3371158.3371182"},{"key":"1727_CR30","unstructured":"Lugaresi C, Tang J, Nash H, McClanahan C, Uboweja E, Hays M, Zhang F, Chang CL, Yong MG, Lee J, et\u00a0al. Mediapipe: A framework for building perception pipelines. arXiv preprint. 2019. arXiv:1906.08172."},{"key":"1727_CR31","unstructured":"Kartynnik Y, Ablavatski A, Grishchenko I, Grundmann M. Real-time facial surface geometry from monocular video on mobile GPUs. arXiv preprint. 2019. arXiv:1907.06724."},{"key":"1727_CR32","unstructured":"Theodoridis S, Koutroumbas K. Pattern recognition. Elsevier. 2006."},{"key":"1727_CR33","unstructured":"Liu H, Motoda H. Feature selection for knowledge discovery and data mining. Springer Science & Business Media. 2012;vol. 454."},{"key":"1727_CR34","doi-asserted-by":"crossref","unstructured":"Cortes C, Vapnik V. Support-vector networks Machine learning. 1995;20(3):273\u201397.","DOI":"10.1007\/BF00994018"},{"issue":"3","key":"1727_CR35","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1023\/A:1018628609742","volume":"9","author":"JA Suykens","year":"1999","unstructured":"Suykens JA, Vandewalle J. Least squares support vector machine classifiers. Neural Process Lett. 1999;9(3):293\u2013300.","journal-title":"Neural Process Lett"},{"key":"1727_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104453","volume":"134","author":"S Maheshwari","year":"2021","unstructured":"Maheshwari S, Sharma RR, Kumar M. Lbp-based information assisted intelligent system for Covid-19 identification. Comput Biol Med. 2021;134: 104453.","journal-title":"Comput Biol Med"},{"key":"1727_CR37","doi-asserted-by":"crossref","unstructured":"Goodfellow IJ, Erhan D, Carrier PL, Courville A, Mirza M, Hamner B, Cukierski W, Tang Y, Thaler D, Lee DH, et\u00a0al. Challenges in representation learning: A report on three machine learning contests. In: International conference on neural information processing. Springer. 2013;pp. 117\u2013124.","DOI":"10.1007\/978-3-642-42051-1_16"},{"key":"1727_CR38","unstructured":"Tan M, Le Q. Efficientnetv2: Smaller models and faster training. In: International Conference on Machine Learning. PMLR. 2021;pp. 10096\u201310106."},{"key":"1727_CR39","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016;pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1727_CR40","doi-asserted-by":"crossref","unstructured":"Chollet F. Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017;pp. 1251\u20131258.","DOI":"10.1109\/CVPR.2017.195"},{"key":"1727_CR41","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016;pp. 2818\u20132826.","DOI":"10.1109\/CVPR.2016.308"},{"key":"1727_CR42","doi-asserted-by":"crossref","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi AA. Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence. 2017.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"1727_CR43","unstructured":"Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint. 2014. arXiv:1409.1556."}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-023-01727-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-023-01727-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-023-01727-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T10:18:44Z","timestamp":1682849924000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-023-01727-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,23]]},"references-count":43,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["1727"],"URL":"https:\/\/doi.org\/10.1007\/s42979-023-01727-y","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,23]]},"assertion":[{"value":"12 September 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose. The authors also have no competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"273"}}