Transfer Learning for Facial Expression Recognition
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...
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MDPI AG
2025-04-01
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| author | Rajesh Kumar Giacomo Corvisieri Tullio Flavio Fici Syed Ibrar Hussain Domenico Tegolo Cesare Valenti |
| author_facet | Rajesh Kumar Giacomo Corvisieri Tullio Flavio Fici Syed Ibrar Hussain Domenico Tegolo Cesare Valenti |
| author_sort | Rajesh Kumar |
| collection | DOAJ |
| description | 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 × 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–patient communication and improving patient care. |
| format | Article |
| id | doaj-art-e32805fef2d345a5afc0433dae656d19 |
| institution | DOAJ |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-e32805fef2d345a5afc0433dae656d192025-08-20T03:13:51ZengMDPI AGInformation2078-24892025-04-0116432010.3390/info16040320Transfer Learning for Facial Expression RecognitionRajesh Kumar0Giacomo Corvisieri1Tullio Flavio Fici2Syed Ibrar Hussain3Domenico Tegolo4Cesare Valenti5Dipartimento di Matematica e Informatica, Università degli Studi di Palermo, Via Archirafi 34, 90123 Palermo, ItalyItaltel S.p.A., Viale Schiavonetti 270/F, 00173 Rome, ItalyItaltel S.p.A., Viale Schiavonetti 270/F, 00173 Rome, ItalyDipartimento di Matematica e Informatica, Università degli Studi di Palermo, Via Archirafi 34, 90123 Palermo, ItalyDipartimento di Matematica e Informatica, Università degli Studi di Palermo, Via Archirafi 34, 90123 Palermo, ItalyDipartimento di Matematica e Informatica, Università degli Studi di Palermo, Via Archirafi 34, 90123 Palermo, ItalyFacial 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 × 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–patient communication and improving patient care.https://www.mdpi.com/2078-2489/16/4/320face detectionfacial expression recognitiondeep learning techniques |
| spellingShingle | Rajesh Kumar Giacomo Corvisieri Tullio Flavio Fici Syed Ibrar Hussain Domenico Tegolo Cesare Valenti Transfer Learning for Facial Expression Recognition Information face detection facial expression recognition deep learning techniques |
| title | Transfer Learning for Facial Expression Recognition |
| title_full | Transfer Learning for Facial Expression Recognition |
| title_fullStr | Transfer Learning for Facial Expression Recognition |
| title_full_unstemmed | Transfer Learning for Facial Expression Recognition |
| title_short | Transfer Learning for Facial Expression Recognition |
| title_sort | transfer learning for facial expression recognition |
| topic | face detection facial expression recognition deep learning techniques |
| url | https://www.mdpi.com/2078-2489/16/4/320 |
| work_keys_str_mv | AT rajeshkumar transferlearningforfacialexpressionrecognition AT giacomocorvisieri transferlearningforfacialexpressionrecognition AT tullioflaviofici transferlearningforfacialexpressionrecognition AT syedibrarhussain transferlearningforfacialexpressionrecognition AT domenicotegolo transferlearningforfacialexpressionrecognition AT cesarevalenti transferlearningforfacialexpressionrecognition |