{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T18:41:07Z","timestamp":1780080067086,"version":"3.54.0"},"publisher-location":"Singapore","reference-count":53,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819967018","type":"print"},{"value":"9789819967025","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-99-6702-5_25","type":"book-chapter","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T18:02:42Z","timestamp":1700503362000},"page":"297-307","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Survey: Classifying and Predicting Features Based on Facial Analysis"],"prefix":"10.1007","author":[{"given":"J.","family":"Tejaashwini Goud","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nuthanakanti","family":"Bhaskar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Voruganti Naresh","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Suraya","family":"Mubeen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonnadula","family":"Narasimharao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raheem","family":"Unnisa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,11,21]]},"reference":[{"key":"25_CR1","doi-asserted-by":"publisher","first-page":"4708","DOI":"10.1016\/j.matpr.2020.08.350","volume":"33","author":"B Abirami","year":"2020","unstructured":"Abirami, B., Subashini, T.S., Mahavaishnavi, V.: Gender and age prediction from real time facial images using CNN. Mater. Today Proc. 33, 4708\u20134712 (2020)","journal-title":"Mater. Today Proc."},{"key":"25_CR2","doi-asserted-by":"crossref","unstructured":"Agbo-Ajala, O., Viriri, S.: Deeply learned classifiers for age and gender predictions of unfiltered faces. Sci. World J. 2020 (2020)","DOI":"10.1155\/2020\/1289408"},{"key":"25_CR3","doi-asserted-by":"crossref","unstructured":"Al Mashagba, E.F.: Real-time gender classification by face. Int. J. Adv. Comput. Sci. Appl. 7(3) (2016)","DOI":"10.14569\/IJACSA.2016.070347"},{"key":"25_CR4","doi-asserted-by":"crossref","unstructured":"Ali, H., et al.: Facial emotion recognition using empirical mode decomposition. Expert Syst. Appl. 42(3), 1261\u20131277 (2015)","DOI":"10.1016\/j.eswa.2014.08.049"},{"key":"25_CR5","doi-asserted-by":"crossref","unstructured":"Alreshidi, A., Ullah, M.: Facial emotion recognition using hybrid features. In: Informatics, vol. 7. no. 1. Multidisciplinary Digital Publishing Institute (2020)","DOI":"10.3390\/informatics7010006"},{"key":"25_CR6","doi-asserted-by":"crossref","unstructured":"Antipov, G., et al.: Effective training of convolutional neural networks for face-based gender and age prediction. Pattern Recogn. 72, 15\u201326 (2017)","DOI":"10.1016\/j.patcog.2017.06.031"},{"issue":"2","key":"25_CR7","doi-asserted-by":"publisher","first-page":"3039","DOI":"10.1007\/s11042-020-09726-4","volume":"80","author":"M Arora","year":"2021","unstructured":"Arora, M., Kumar, M.: AutoFER: PCA and PSO based automatic facial emotion recognition. Multimedia Tools Appl. 80(2), 3039\u20133049 (2021)","journal-title":"Multimedia Tools Appl."},{"key":"25_CR8","unstructured":"Aslam, T., et al.: Emotion based facial expression detection using machine learning. Life Sci. J. 17(8), 35\u201343 (2020)"},{"key":"25_CR9","doi-asserted-by":"crossref","unstructured":"Bellamkonda, S., Gopalan, N.P.: A facial expression recognition model using support vector machines. IJ Math. Sci. Comput. 4, 56\u201365 (2018)","DOI":"10.5815\/ijmsc.2018.04.05"},{"key":"25_CR10","doi-asserted-by":"publisher","unstructured":"Bhaskar, N., Ganashree, T.S., Patra, T.S.: Pulmonary lung nodule detection and classification through image enhancement and deep learning. Int. J. Biom. 1(1), 1 (2023). https:\/\/doi.org\/10.1504\/IJBM.2023.10044525","DOI":"10.1504\/IJBM.2023.10044525"},{"key":"25_CR11","doi-asserted-by":"crossref","unstructured":"Bukar, A.M., Ugail, H., Connah, D.: Automatic age and gender classification using supervised appearance model. J. Electron. Imaging 25(6), 061605 (2016)","DOI":"10.1117\/1.JEI.25.6.061605"},{"issue":"4","key":"25_CR12","doi-asserted-by":"publisher","first-page":"5059","DOI":"10.1007\/s11042-017-5241-5","volume":"77","author":"JK Chang","year":"2018","unstructured":"Chang, J.K., Ryoo, S.T.: Implementation of an improved facial emotion retrieval method in multimedia system. Multimedia Tools Appl. 77(4), 5059\u20135065 (2018)","journal-title":"Multimedia Tools Appl."},{"key":"25_CR13","doi-asserted-by":"crossref","unstructured":"Dagher, I., Dahdah, E., Al Shakik, M.: Facial expression recognition using three-stage support vector machines. Visual Comput. Indus. Biomed. Art 2(1), 1\u20139 (2019)","DOI":"10.1186\/s42492-019-0034-5"},{"issue":"1","key":"25_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-020-00289-7","volume":"7","author":"V Doma","year":"2020","unstructured":"Doma, V., Pirouz, M.: A comparative analysis of machine learning methods for emotion recognition using EEG and peripheral physiological signals. J. Big Data 7(1), 1\u201321 (2020)","journal-title":"J. Big Data"},{"key":"25_CR15","doi-asserted-by":"crossref","unstructured":"Duan, M., et al.: A hybrid deep learning CNN\u2013ELM for age and gender classification. Neurocomputing 275, 448\u2013461 (2018)","DOI":"10.1016\/j.neucom.2017.08.062"},{"key":"25_CR16","doi-asserted-by":"crossref","unstructured":"Fang, J., et al.: Muti-stage learning for gender and age prediction. Neurocomputing 334, 114\u2013124 (2019)","DOI":"10.1016\/j.neucom.2018.12.073"},{"key":"25_CR17","doi-asserted-by":"crossref","unstructured":"Ghimire, D., et al.: Facial expression recognition based on local region specific features and support vector machines. Multimedia Tools Appl. 76(6), 7803\u20137821 (2017)","DOI":"10.1007\/s11042-016-3418-y"},{"key":"25_CR18","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1016\/j.neunet.2020.07.006","volume":"130","author":"O Guehairia","year":"2020","unstructured":"Guehairia, O., et al.: Feature fusion via deep random forest for facial age estimation. Neural Netw. 130, 238\u2013252 (2020)","journal-title":"Neural Netw."},{"key":"25_CR19","doi-asserted-by":"crossref","unstructured":"Hassouneh, A., Mutawa, A.M., Murugappan, M.: Development of a real-time emotion recognition system using facial expressions and EEG based on machine learning and deep neural network methods. Inform. Med. Unlocked 20, 100372 (2020)","DOI":"10.1016\/j.imu.2020.100372"},{"key":"25_CR20","doi-asserted-by":"crossref","unstructured":"Kaushik, P., et al.: EEG-based age and gender prediction using deep BLSTM-LSTM network model. IEEE Sens. J. 19(7), 2634\u20132641 (2018)","DOI":"10.1109\/JSEN.2018.2885582"},{"key":"25_CR21","doi-asserted-by":"crossref","unstructured":"Khan, K., et al.: A unified framework for head pose, age and gender classification through end-to-end face segmentation. Entropy 21(7), 647 (2019)","DOI":"10.3390\/e21070647"},{"key":"25_CR22","doi-asserted-by":"crossref","unstructured":"Khan, K., et al.: Automatic gender classification through face segmentation. Symmetry 11(6), 770 (2019)","DOI":"10.3390\/sym11060770"},{"key":"25_CR23","doi-asserted-by":"crossref","unstructured":"Kumar, S., et al.: Exploring impact of age and gender on sentiment analysis using machine learning. Electronics 9(2), 374 (2020)","DOI":"10.3390\/electronics9020374"},{"key":"25_CR24","doi-asserted-by":"publisher","unstructured":"Morampudi, M.K., Gonthina, N., Bhaskar, N., Dinesh Reddy, V.: Image description generator using residual neural network and long short-term memory. Comput. Sci. J. Moldova 31(1(91)), 3\u201321 (2023). https:\/\/doi.org\/10.56415\/csjm.v31.01","DOI":"10.56415\/csjm.v31.01"},{"key":"25_CR25","unstructured":"Perveen, N., et al.: Facial expression recognition through machine learning. Int. J. Sci. Technol. Res. 5(03) (2016)"},{"issue":"5","key":"25_CR26","doi-asserted-by":"publisher","first-page":"126","DOI":"10.3390\/fi13050126","volume":"13","author":"J R\u00f6\u00dfler","year":"2021","unstructured":"R\u00f6\u00dfler, J., Sun, J., Gloor, P.: Reducing videoconferencing fatigue through facial emotion recognition. Future Internet 13(5), 126 (2021)","journal-title":"Future Internet"},{"key":"25_CR27","doi-asserted-by":"crossref","unstructured":"Salido Ortega, M.G., Rodr\u00edguez, L.F., Gutierrez-Garcia, J.O.: Towards emotion recognition from contextual information using machine learning. J. Ambient Intell. Hum. Comput. 11(8), 3187\u20133207 (2020)","DOI":"10.1007\/s12652-019-01485-x"},{"key":"25_CR28","doi-asserted-by":"crossref","unstructured":"Singh, A., et al.: Age, gender prediction and emotion recognition using convolutional neural network. Available at SSRN 3833759 (2021)","DOI":"10.2139\/ssrn.3833759"},{"issue":"13","key":"25_CR29","doi-asserted-by":"publisher","first-page":"4389","DOI":"10.1007\/s00500-017-2634-3","volume":"22","author":"H-H Tsai","year":"2018","unstructured":"Tsai, H.-H., Chang, Y.-C.: Facial expression recognition using a combination of multiple facial features and support vector machine. Soft Comput. 22(13), 4389\u20134405 (2018)","journal-title":"Soft Comput."},{"issue":"3","key":"25_CR30","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1109\/TCYB.2015.2416321","volume":"46","author":"S Wang","year":"2015","unstructured":"Wang, S., Tao, D., Yang, J.: Relative attribute SVM+ learning for age estimation. IEEE Trans. Cybern. 46(3), 827\u2013839 (2015)","journal-title":"IEEE Trans. Cybern."},{"key":"25_CR31","doi-asserted-by":"crossref","unstructured":"Wang, F., et al.: Facial expression recognition from image based on hybrid features understanding. J. Vis. Commun. Image Representation 59, 84\u201388 (2019)","DOI":"10.1016\/j.jvcir.2018.11.010"},{"key":"25_CR32","unstructured":"Wanga, G., Davies, S.R.: Deep machine learning for age and gender prediction. ICTACT J. Soft Comput. (2019)"},{"key":"25_CR33","doi-asserted-by":"crossref","unstructured":"Yuan, Z.: Face detection and recognition based on visual attention mechanism guidance model in unrestricted posture. Sci. Programm. 2020 (2020)","DOI":"10.1155\/2020\/8861987"},{"key":"25_CR34","doi-asserted-by":"crossref","unstructured":"Zaghbani, S., Noureddine B., Bouhlel, M.S.: Age estimation using deep learning. Comput. Electr. Eng. 68, 337\u2013347 (2018)","DOI":"10.1016\/j.compeleceng.2018.04.012"},{"key":"25_CR35","unstructured":"http:\/\/whdeng.cn\/RAF\/model1.html"},{"key":"25_CR36","unstructured":"https:\/\/areeweb.polito.it\/ricerca\/cgvg\/siblingsDB.html"},{"key":"25_CR37","unstructured":"https:\/\/bml.ym.edu.tw\/tfeid\/"},{"key":"25_CR38","unstructured":"https:\/\/computervisiononline.com\/dataset\/1105138612"},{"key":"25_CR39","unstructured":"https:\/\/cvhci.anthropomatik.kit.edu\/433_451.php"},{"key":"25_CR40","unstructured":"https:\/\/data.fei.org\/Default.aspx"},{"key":"25_CR41","unstructured":"https:\/\/data.vision.ee.ethz.ch\/cvl\/rrothe\/imdb-wiki\/"},{"key":"25_CR42","unstructured":"https:\/\/mmifacedb.eu\/"},{"key":"25_CR43","unstructured":"https:\/\/paperswithcode.com\/dataset\/adience"},{"key":"25_CR44","unstructured":"https:\/\/paperswithcode.com\/dataset\/fg-net"},{"key":"25_CR45","unstructured":"https:\/\/paperswithcode.com\/dataset\/morph"},{"key":"25_CR46","unstructured":"https:\/\/rafd.socsci.ru.nl\/RaFD2\/RaFD?p=main"},{"key":"25_CR47","unstructured":"https:\/\/researchdata.edu.au\/static-facial-expressions-wild-sfew\/2729"},{"key":"25_CR48","unstructured":"https:\/\/uncw.edu\/oic\/tech\/morph_academic.html"},{"key":"25_CR49","unstructured":"https:\/\/vis-www.cs.umass.edu\/lfw\/"},{"key":"25_CR50","unstructured":"https:\/\/www.dartmouth.edu\/oir\/data-reporting\/cds\/index.html"},{"key":"25_CR51","unstructured":"https:\/\/www.jeffcohn.net\/Resources\/"},{"key":"25_CR52","unstructured":"https:\/\/www.kaggle.com\/datasets\/msambare\/fer2013"},{"key":"25_CR53","unstructured":"https:\/\/zenodo.org\/record\/3451524#.Y_xDD3ZBzIU"}],"container-title":["Smart Innovation, Systems and Technologies","Evolution in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-6702-5_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,9]],"date-time":"2024-10-09T06:09:18Z","timestamp":1728454158000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-6702-5_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819967018","9789819967025"],"references-count":53,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-6702-5_25","relation":{},"ISSN":["2190-3018","2190-3026"],"issn-type":[{"value":"2190-3018","type":"print"},{"value":"2190-3026","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"21 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FICTA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Frontiers of Intelligent Computing: Theory and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Cardiff","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ficta2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ficta.co.uk\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}