{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T03:17:24Z","timestamp":1776309444592,"version":"3.50.1"},"reference-count":78,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T00:00:00Z","timestamp":1744848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"FFR 2024 Cesare Valenti","award":["PNRR-I-M4C2-I1.3"],"award-info":[{"award-number":["PNRR-I-M4C2-I1.3"]}]},{"name":"FFR 2024 Cesare Valenti","award":["B73C22001250006"],"award-info":[{"award-number":["B73C22001250006"]}]},{"name":"European Union\u2019s NextGenerationEU","award":["PNRR-I-M4C2-I1.3"],"award-info":[{"award-number":["PNRR-I-M4C2-I1.3"]}]},{"name":"European Union\u2019s NextGenerationEU","award":["B73C22001250006"],"award-info":[{"award-number":["B73C22001250006"]}]},{"name":"\u201cHEAL ITALIA\u201d","award":["PNRR-I-M4C2-I1.3"],"award-info":[{"award-number":["PNRR-I-M4C2-I1.3"]}]},{"name":"\u201cHEAL ITALIA\u201d","award":["B73C22001250006"],"award-info":[{"award-number":["B73C22001250006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Facial expressions reflect psychological states and are crucial for understanding human emotions. Traditional facial expression recognition methods face challenges in real-world healthcare applications due to variations in facial structure, lighting conditions and occlusion. We present a methodology based on transfer learning with the pre-trained models VGG-19 and ResNet-152, and we highlight dataset-specific preprocessing techniques that include resizing images to 124 \u00d7 124 pixels, augmenting the data and selectively freezing layers to enhance the robustness of the model. This study explores the application of deep learning-based facial expression recognition in healthcare, particularly for remote patient monitoring and telemedicine, where accurate facial expression recognition can enhance patient assessment and early diagnosis of psychological conditions such as depression and anxiety. The proposed method achieved an average accuracy of 0.98 on the CK+ dataset, demonstrating its effectiveness in controlled environments. However performance varied across datasets, with accuracy rates of 0.44 on FER2013 and 0.89 on JAFFE, reflecting the challenges posed by noisy and diverse data. Our findings emphasize the potential of deep learning-based facial expression recognition in healthcare applications while underscoring the importance of dataset-specific model optimization to improve generalization across different data distributions. This research contributes to the advancement of automated facial expression recognition in telemedicine, supporting enhanced doctor\u2013patient communication and improving patient care.<\/jats:p>","DOI":"10.3390\/info16040320","type":"journal-article","created":{"date-parts":[[2025,4,17]],"date-time":"2025-04-17T20:05:56Z","timestamp":1744920356000},"page":"320","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Transfer Learning for Facial Expression Recognition"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8223-828X","authenticated-orcid":false,"given":"Rajesh","family":"Kumar","sequence":"first","affiliation":[{"name":"Dipartimento di Matematica e Informatica, Universit\u00e0 degli Studi di Palermo, Via Archirafi 34, 90123 Palermo, Italy"}]},{"given":"Giacomo","family":"Corvisieri","sequence":"additional","affiliation":[{"name":"Italtel S.p.A., Viale Schiavonetti 270\/F, 00173 Rome, Italy"}]},{"given":"Tullio Flavio","family":"Fici","sequence":"additional","affiliation":[{"name":"Italtel S.p.A., Viale Schiavonetti 270\/F, 00173 Rome, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9391-3858","authenticated-orcid":false,"given":"Syed Ibrar","family":"Hussain","sequence":"additional","affiliation":[{"name":"Dipartimento di Matematica e Informatica, Universit\u00e0 degli Studi di Palermo, Via Archirafi 34, 90123 Palermo, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5417-5584","authenticated-orcid":false,"given":"Domenico","family":"Tegolo","sequence":"additional","affiliation":[{"name":"Dipartimento di Matematica e Informatica, Universit\u00e0 degli Studi di Palermo, Via Archirafi 34, 90123 Palermo, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4961-2054","authenticated-orcid":false,"given":"Cesare","family":"Valenti","sequence":"additional","affiliation":[{"name":"Dipartimento di Matematica e Informatica, Universit\u00e0 degli Studi di Palermo, Via Archirafi 34, 90123 Palermo, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Shan, K., Guo, J., You, W., Lu, D., and Bie, R. 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