{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T04:28:12Z","timestamp":1773808092729,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T00:00:00Z","timestamp":1695686400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T00:00:00Z","timestamp":1695686400000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-16717-8","type":"journal-article","created":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T03:29:08Z","timestamp":1695698948000},"page":"33161-33184","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Empowering facial emotion recognition in service industry \u2013 a two-stage convolutional neural network model"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5404-5023","authenticated-orcid":false,"given":"Kung-Jeng","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ching-Ning","family":"Hsu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lucy","family":"Sanjaya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,26]]},"reference":[{"key":"16717_CR1","doi-asserted-by":"publisher","first-page":"102551","DOI":"10.1016\/j.jretconser.2021.102551","volume":"61","author":"SHW Chuah","year":"2021","unstructured":"Chuah SHW, Yu J (2021) The future of service: the power of emotion in human-robot interaction. J Retail Consum Serv 61:102551","journal-title":"J Retail Consum Serv"},{"issue":"20","key":"16717_CR2","doi-asserted-by":"publisher","first-page":"29607","DOI":"10.1007\/s11042-019-07813-9","volume":"78","author":"K Shrivastava","year":"2019","unstructured":"Shrivastava K, Kumar S, Jain DK (2019) An effective approach for emotion detection in multimedia text data using sequence based convolutional neural network. Multimed Tools Appl 78(20):29607\u201329639","journal-title":"Multimed Tools Appl"},{"key":"16717_CR3","doi-asserted-by":"publisher","first-page":"107861","DOI":"10.1016\/j.asoc.2021.107861","volume":"113","author":"YC Chang","year":"2021","unstructured":"Chang YC, Hsing YC (2021) Emotion-infused deep neural network for emotionally resonant conversation. Appl Soft Comput 113:107861","journal-title":"Appl Soft Comput"},{"key":"16717_CR4","doi-asserted-by":"publisher","first-page":"107152","DOI":"10.1016\/j.asoc.2021.107152","volume":"103","author":"Y Bi","year":"2021","unstructured":"Bi Y, Xue B, Zhang M (2021) Multi-objective genetic programming for feature learning in face recognition. Appl Soft Comput 103:107152","journal-title":"Appl Soft Comput"},{"issue":"1","key":"16717_CR5","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1177\/1094670520902266","volume":"24","author":"MH Huang","year":"2021","unstructured":"Huang MH, Rust RT (2021) Engaged to a robot? The role of AI in service. J Serv Res 24(1):30\u201341","journal-title":"J Serv Res"},{"key":"16717_CR6","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.inffus.2017.02.003","volume":"37","author":"S Poria","year":"2017","unstructured":"Poria S, Cambria E, Bajpai R, Hussain A (2017) A review of affective computing: from unimodal analysis to multimodal fusion. Inf Fus 37:98\u2013125","journal-title":"Inf Fus"},{"key":"16717_CR7","doi-asserted-by":"publisher","first-page":"107101","DOI":"10.1016\/j.asoc.2021.107101","volume":"102","author":"S Kwon","year":"2021","unstructured":"Kwon S (2021) Att-net: enhanced emotion recognition system using lightweight self-attention module. Appl Soft Comput 102:107101","journal-title":"Appl Soft Comput"},{"issue":"2","key":"16717_CR8","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1037\/h0030377","volume":"17","author":"P Ekman","year":"1971","unstructured":"Ekman P, Friesen WV (1971) Constants across cultures in the face and emotion. J Pers Soc Psychol 17(2):124","journal-title":"J Pers Soc Psychol"},{"key":"16717_CR9","doi-asserted-by":"crossref","unstructured":"Ghofrani A, Toroghi RM, Ghanbari S (2019) Realtime face-detection and emotion recognition using mtcnn and minishufflenet v2. In 2019 5th conference on knowledge based engineering and innovation (KBEI) (pp 817\u2013821). IEEE","DOI":"10.1109\/KBEI.2019.8734924"},{"issue":"1","key":"16717_CR10","doi-asserted-by":"publisher","first-page":"012158","DOI":"10.1088\/1742-6596\/1651\/1\/012158","volume":"1651","author":"C Su","year":"2020","unstructured":"Su C, Wang G (2020) Design and application of learner emotion recognition for classroom. J Phys Conf Ser 1651(1):012158 IOP Publishing","journal-title":"J Phys Conf Ser"},{"key":"16717_CR11","doi-asserted-by":"publisher","first-page":"174922","DOI":"10.1109\/ACCESS.2020.3023782","volume":"8","author":"X Li","year":"2020","unstructured":"Li X, Yang Z, Wu H (2020) Face detection based on receptive field enhanced multi-task cascaded convolutional neural networks. IEEE Access 8:174922\u2013174930","journal-title":"IEEE Access"},{"key":"16717_CR12","unstructured":"Redmon J, Farhadi A (2018) YOLOv3: An Incremental Improvement. arXiv pre-print server. arxiv:1804.02767"},{"key":"16717_CR13","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1016\/j.jbusres.2019.08.038","volume":"116","author":"S Robinson","year":"2020","unstructured":"Robinson S, Orsingher C, Alkire L, De Keyser A, Giebelhausen M, Papamichail KN, \u2026 Temerak MS (2020) Frontline encounters of the AI kind: an evolved service encounter framework. J Bus Res 116:366\u2013376","journal-title":"J Bus Res"},{"key":"16717_CR14","doi-asserted-by":"publisher","first-page":"102186","DOI":"10.1016\/j.jretconser.2020.102186","volume":"56","author":"C Prentice","year":"2020","unstructured":"Prentice C, Nguyen M (2020) Engaging and retaining customers with AI and employee service. J Retail Consum Serv 56:102186","journal-title":"J Retail Consum Serv"},{"issue":"1","key":"16717_CR15","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1177\/1094670520975110","volume":"24","author":"LD Hollebeek","year":"2021","unstructured":"Hollebeek LD, Sprott DE, Brady MK (2021) Rise of the machines? Customer engagement in automated service interactions. J Serv Res 24(1):3\u20138","journal-title":"J Serv Res"},{"key":"16717_CR16","doi-asserted-by":"crossref","unstructured":"Henkel AP, Bromuri S, Iren D, Urovi V (2020) Half human, half machine\u2013augmenting service employees with AI for interpersonal emotion regulation. J Serv Manag 31(2)","DOI":"10.1108\/JOSM-05-2019-0160"},{"key":"16717_CR17","unstructured":"Ekman P, Friesen WV (2003) Unmasking the face: a guide to recognizing emotions from facial clues (Vol. 10). Ishk"},{"issue":"2","key":"16717_CR18","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/j.jneumeth.2011.06.023","volume":"200","author":"J Hamm","year":"2011","unstructured":"Hamm J, Kohler CG, Gur RC, Verma R (2011) Automated facial action coding system for dynamic analysis of facial expressions in neuropsychiatric disorders. J Neurosci Methods 200(2):237\u2013256","journal-title":"J Neurosci Methods"},{"issue":"4","key":"16717_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.15830\/kjm.2020.35.4.1","volume":"35","author":"RP Bagozzi","year":"2020","unstructured":"Bagozzi RP (2020a) Foundations of emotional research and its application. Korean J Market 35(4):1\u201351","journal-title":"Korean J Market"},{"key":"16717_CR20","unstructured":"Kotler P, Kartajaya H, Setiawan I (2021) Marketing 5.0: technology for humanity. John Wiley & Sons"},{"key":"16717_CR21","doi-asserted-by":"crossref","unstructured":"Mehrabian A (2017) Nonverbal communication. Routledge","DOI":"10.4324\/9781351308724"},{"issue":"2","key":"16717_CR22","doi-asserted-by":"publisher","first-page":"401","DOI":"10.3390\/s18020401","volume":"18","author":"BC Ko","year":"2018","unstructured":"Ko BC (2018) A brief review of facial emotion recognition based on visual information. Sensors 18(2):401","journal-title":"Sensors"},{"key":"16717_CR23","doi-asserted-by":"publisher","first-page":"5723","DOI":"10.1109\/ACCESS.2017.2686424","volume":"5","author":"J Lei","year":"2017","unstructured":"Lei J, Han Q, Chen L, Lai Z, Zeng L, Liu X (2017) A novel side face contour extraction algorithm for driving fatigue statue recognition. IEEE Access 5:5723\u20135730","journal-title":"IEEE Access"},{"key":"16717_CR24","doi-asserted-by":"publisher","first-page":"4630","DOI":"10.1109\/ACCESS.2017.2784096","volume":"6","author":"B Yang","year":"2017","unstructured":"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\u20134640","journal-title":"IEEE Access"},{"issue":"2","key":"16717_CR25","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1007\/s12193-015-0209-0","volume":"10","author":"BK Kim","year":"2016","unstructured":"Kim BK, Roh J, Dong SY, Lee SY (2016) Hierarchical committee of deep convolutional neural networks for robust facial expression recognition. J Multimod User Interfaces 10(2):173\u2013189","journal-title":"J Multimod User Interfaces"},{"key":"16717_CR26","doi-asserted-by":"crossref","unstructured":"Huang R, Pedoeem J, Chen C (2018) YOLO-LITE: a real-time object detection algorithm optimized for non-GPU computers. In 2018 IEEE international conference on big data (big data) (pp 2503\u20132510). IEEE","DOI":"10.1109\/BigData.2018.8621865"},{"key":"16717_CR27","doi-asserted-by":"publisher","first-page":"101643","DOI":"10.1016\/j.aei.2022.101643","volume":"52","author":"KJ Wang","year":"2022","unstructured":"Wang KJ, Asrini LJ, Sanjaya L, Nguyen PH (2022) Model for deep learning-based skill transfer in an assembly process. Adv Eng Inform 52:101643","journal-title":"Adv Eng Inform"},{"issue":"27","key":"16717_CR28","doi-asserted-by":"publisher","first-page":"19629","DOI":"10.1007\/s11042-020-08841-6","volume":"79","author":"MC Lee","year":"2020","unstructured":"Lee MC, Chiang SY, Yeh SC, Wen TF (2020) Study on emotion recognition and companion Chatbot using deep neural network. Multimed Tools Appl 79(27):19629\u201319657","journal-title":"Multimed Tools Appl"},{"key":"16717_CR29","doi-asserted-by":"crossref","unstructured":"Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001 (Vol. 1, pp I-I). IEEE","DOI":"10.1109\/CVPR.2001.990517"},{"issue":"10","key":"16717_CR30","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang K, Zhang Z, Li Z, Qiao Y (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Process Lett 23(10):1499\u20131503","journal-title":"IEEE Signal Process Lett"},{"key":"16717_CR31","unstructured":"Casalboni A, Qin M, Sabo I, Hawkins A, Alapati S, Badola V, ..., Cecaro F (2017) Google Vision vs. Amazon Rekognition: A Vendor-Neutral Comparison. Cloud Academy"},{"key":"16717_CR32","unstructured":"Evergreen (2021) Facial recognition services review https:\/\/evergreen.team\/articles\/facial-recognition-services-comparison.html. Accessed 10 May 2022"},{"key":"16717_CR33","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/978-3-642-42051-1_16","volume-title":"International conference on neural information processing","author":"IJ Goodfellow","year":"2013","unstructured":"Goodfellow IJ, Erhan D, Carrier PL, Courville A, Mirza M, Hamner B, \u2026 Bengio Y (2013) Challenges in representation learning: a report on three machine learning contests. In: International conference on neural information processing. Springer, Berlin, Heidelberg, pp 117\u2013124"},{"key":"16717_CR34","doi-asserted-by":"crossref","unstructured":"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 (pp 2852-2861)","DOI":"10.1109\/CVPR.2017.277"},{"key":"16717_CR35","doi-asserted-by":"crossref","unstructured":"Lucey P, Cohn JF, Kanade T, Saragih J, Ambadar Z, Matthews I (2010) 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 (pp 94\u2013101). IEEE","DOI":"10.1109\/CVPRW.2010.5543262"},{"key":"16717_CR36","unstructured":"Bochkovskiy A, Wang CY, Liao HYM (2020) Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934"},{"key":"16717_CR37","first-page":"1","volume-title":"Advances in hybridization of intelligent methods","author":"P Giannopoulos","year":"2018","unstructured":"Giannopoulos P, Perikos I, Hatzilygeroudis I (2018) Deep learning approaches for facial emotion recognition: a case study on FER-2013. In: Advances in hybridization of intelligent methods. Springer, Cham, pp 1\u201316"},{"issue":"6","key":"16717_CR38","doi-asserted-by":"publisher","first-page":"1678","DOI":"10.3390\/s20061678","volume":"20","author":"L Pang","year":"2020","unstructured":"Pang L, Liu H, Chen Y, Miao J (2020) Real-time concealed object detection from passive millimeter wave images based on the YOLOv3 algorithm. Sensors 20(6):1678","journal-title":"Sensors"},{"issue":"3","key":"16717_CR39","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1109\/MMUL.2012.26","volume":"19","author":"A Dhall","year":"2012","unstructured":"Dhall A, Goecke R, Lucey S, Gedeon T (2012) Collecting large, richly annotated facial-expression databases from movies. IEEE MultiMed 19(3):34\u201341","journal-title":"IEEE MultiMed"},{"key":"16717_CR40","doi-asserted-by":"crossref","unstructured":"Kossaifi J, Tzimiropoulos G, Todorovic S, Pantic M (2017) AFEW-VA database for valence and arousal estimation in-the-wild, image and vision computing","DOI":"10.1016\/j.imavis.2017.02.001"},{"key":"16717_CR41","doi-asserted-by":"crossref","unstructured":"Vielzeuf V, Pateux S, Jurie F (2017) Temporal multimodal fusion for video emotion classification in the wild. arXiv:1709.07200","DOI":"10.1145\/3136755.3143011"},{"issue":"5","key":"16717_CR42","doi-asserted-by":"publisher","first-page":"552","DOI":"10.1108\/09564230810903460","volume":"19","author":"M S\u00f6derlund","year":"2008","unstructured":"S\u00f6derlund M, Rosengren S (2008) Revisiting the smiling service worker and customer satisfaction. Int J Serv Ind Manag 19(5):552\u2013574","journal-title":"Int J Serv Ind Manag"},{"key":"16717_CR43","doi-asserted-by":"publisher","first-page":"101404","DOI":"10.1016\/j.tele.2020.101404","volume":"51","author":"MR Gonz\u00e1lez-Rodr\u00edguez","year":"2020","unstructured":"Gonz\u00e1lez-Rodr\u00edguez MR, D\u00edaz-Fern\u00e1ndez MC, G\u00f3mez CP (2020) Facial-expression recognition: an emergent approach to the measurement of tourist satisfaction through emotions. Telematics Inform 51:101404","journal-title":"Telematics Inform"},{"key":"16717_CR44","doi-asserted-by":"crossref","unstructured":"Indira DNVSLS, Sumalatha L, Markapudi BR (2021) Multi facial expression recognition (MFER) for identifying customer satisfaction on products using deep CNN and Haar Cascade classifier. In IOP conference series: materials science and engineering (Vol. 1074, No. 1, p 012033). IOP Publishing","DOI":"10.1088\/1757-899X\/1074\/1\/012033"},{"key":"16717_CR45","doi-asserted-by":"crossref","unstructured":"Singh G, Slack N, Sharma S, Mudaliar K, Narayan S, Kaur R, Sharma KU (2021) Antecedents involved in developing fast-food restaurant customer loyalty. The TQM J","DOI":"10.1108\/TQM-07-2020-0163"},{"issue":"1\u20132","key":"16717_CR46","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1080\/02642069.2020.1863373","volume":"41","author":"K Yang","year":"2021","unstructured":"Yang K, Kim J, Min J, Hernandez-Calderon A (2021) Effects of retailers\u2019 service quality and legitimacy on behavioral intention: the role of emotions during COVID-19. Serv Ind J 41(1\u20132):84\u2013106","journal-title":"Serv Ind J"},{"key":"16717_CR47","unstructured":"American Heart Association (2017) https:\/\/www.heart.org\/. Accessed 10 May 2022"},{"issue":"6","key":"16717_CR48","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1509\/jmkr.46.6.777","volume":"46","author":"DA Small","year":"2009","unstructured":"Small DA, Verrochi NM (2009) The face of need: facial emotion expression on charity advertisements. J Mark Res 46(6):777\u2013787","journal-title":"J Mark Res"},{"issue":"1","key":"16717_CR49","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1177\/1094670507304694","volume":"10","author":"KS Dallimore","year":"2007","unstructured":"Dallimore KS, Sparks BA, Butcher K (2007) The influence of angry customer outbursts on service providers' facial displays and affective states. J Serv Res 10(1):78\u201392","journal-title":"J Serv Res"},{"issue":"1","key":"16717_CR50","first-page":"57","volume":"6","author":"M Perusqu\u00eda-Hern\u00e1ndez","year":"2021","unstructured":"Perusqu\u00eda-Hern\u00e1ndez M (2021) Are people happy when they smile?: affective assessments based on automatic smile genuineness identification. Emot Stud 6(1):57\u201371","journal-title":"Emot Stud"},{"key":"16717_CR51","unstructured":"NVIDIA (2021) The NVIDIA transfer learning toolkit.\u00a0https:\/\/docs.nvidia.com\/tlt\/tlt-user-guide\/text\/object_detection\/yolo_v3.html. Accessed 10 May 2022"},{"key":"16717_CR52","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Houlsby N (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.\u00a0Accessed 10 May 2022"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16717-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-16717-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-16717-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T23:37:39Z","timestamp":1730158659000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-16717-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,26]]},"references-count":52,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["16717"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-16717-8","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,26]]},"assertion":[{"value":"27 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 August 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 September 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All the authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interests\/competing interests"}}]}}