{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T17:13:55Z","timestamp":1773767635092,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T00:00:00Z","timestamp":1737331200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>This study explores children\u2019s emotions through a novel approach of Generative Artificial Intelligence (GenAI) and Facial Muscle Activation (FMA). It examines GenAI\u2019s effectiveness in creating facial images that produce genuine emotional responses in children, alongside FMA\u2019s analysis of muscular activation during these expressions. The aim is to determine if AI can realistically generate and recognize emotions similar to human experiences. The study involves generating a database of 280 images (40 per emotion) of children expressing various emotions. For real children\u2019s faces from public databases (DEFSS and NIMH-CHEFS), five emotions were considered: happiness, angry, fear, sadness, and neutral. In contrast, for AI-generated images, seven emotions were analyzed, including the previous five plus surprise and disgust. A feature vector is extracted from these images, indicating lengths between reference points on the face that contract or expand based on the expressed emotion. This vector is then input into an artificial neural network for emotion recognition and classification, achieving accuracies of up to 99% in certain cases. This approach offers new avenues for training and validating AI algorithms, enabling models to be trained with artificial and real-world data interchangeably. The integration of both datasets during training and validation phases enhances model performance and adaptability.<\/jats:p>","DOI":"10.3390\/bdcc9010015","type":"journal-article","created":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T04:04:12Z","timestamp":1737345852000},"page":"15","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Eliciting Emotions: Investigating the Use of Generative AI and Facial Muscle Activation in Children\u2019s Emotional Recognition"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4069-2083","authenticated-orcid":false,"given":"Manuel A.","family":"Solis-Arrazola","sequence":"first","affiliation":[{"name":"Department of Electronics Engineering, DICIS-University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 kms, Salamanca 36885, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5431-6954","authenticated-orcid":false,"given":"Raul E.","family":"Sanchez-Yanez","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, DICIS-University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 kms, Salamanca 36885, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8007-3852","authenticated-orcid":false,"given":"Ana M. S.","family":"Gonzalez-Acosta","sequence":"additional","affiliation":[{"name":"Laboratory of Artificial Intelligence, Robotics and Control, Faculty of Biological Systems and Technological Innovations, Benito Ju\u00e1rez Autonomous University of Oaxaca, Av. Universidad S\/N, Ex-Hacienda 5 Se\u00f1ores, Oaxaca 68120, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1631-0738","authenticated-orcid":false,"given":"Carlos H.","family":"Garcia-Capulin","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, DICIS-University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 kms, Salamanca 36885, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7530-9027","authenticated-orcid":false,"given":"Horacio","family":"Rostro-Gonzalez","sequence":"additional","affiliation":[{"name":"Department of Electronics Engineering, DICIS-University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 kms, Salamanca 36885, Mexico"},{"name":"GEPI Research Group, IQS-School of Engineering, Ramon Llull University, Via Augusta 390, 08017 Barcelona, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100333","DOI":"10.1016\/j.jik.2023.100333","article-title":"A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities","volume":"8","author":"Ali","year":"2023","journal-title":"J. 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