Eliciting Emotions: Investigating the Use of Generative AI and Facial Muscle Activation in Children’s Emotional Recognition
This study explores children’s emotions through a novel approach of Generative Artificial Intelligence (GenAI) and Facial Muscle Activation (FMA). It examines GenAI’s effectiveness in creating facial images that produce genuine emotional responses in children, alongside FMA’s analysis of muscular ac...
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MDPI AG
2025-01-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/9/1/15 |
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| author | Manuel A. Solis-Arrazola Raul E. Sanchez-Yanez Ana M. S. Gonzalez-Acosta Carlos H. Garcia-Capulin Horacio Rostro-Gonzalez |
| author_facet | Manuel A. Solis-Arrazola Raul E. Sanchez-Yanez Ana M. S. Gonzalez-Acosta Carlos H. Garcia-Capulin Horacio Rostro-Gonzalez |
| author_sort | Manuel A. Solis-Arrazola |
| collection | DOAJ |
| description | This study explores children’s emotions through a novel approach of Generative Artificial Intelligence (GenAI) and Facial Muscle Activation (FMA). It examines GenAI’s effectiveness in creating facial images that produce genuine emotional responses in children, alongside FMA’s 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’s 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. |
| format | Article |
| id | doaj-art-d4c2a7ade0584780b4e280b541efc1fd |
| institution | Kabale University |
| issn | 2504-2289 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-d4c2a7ade0584780b4e280b541efc1fd2025-01-24T13:22:33ZengMDPI AGBig Data and Cognitive Computing2504-22892025-01-01911510.3390/bdcc9010015Eliciting Emotions: Investigating the Use of Generative AI and Facial Muscle Activation in Children’s Emotional RecognitionManuel A. Solis-Arrazola0Raul E. Sanchez-Yanez1Ana M. S. Gonzalez-Acosta2Carlos H. Garcia-Capulin3Horacio Rostro-Gonzalez4Department of Electronics Engineering, DICIS-University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 kms, Salamanca 36885, MexicoDepartment of Electronics Engineering, DICIS-University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 kms, Salamanca 36885, MexicoLaboratory of Artificial Intelligence, Robotics and Control, Faculty of Biological Systems and Technological Innovations, Benito Juárez Autonomous University of Oaxaca, Av. Universidad S/N, Ex-Hacienda 5 Señores, Oaxaca 68120, MexicoDepartment of Electronics Engineering, DICIS-University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 kms, Salamanca 36885, MexicoDepartment of Electronics Engineering, DICIS-University of Guanajuato, Carretera Salamanca-Valle de Santiago km 3.5 + 1.8 kms, Salamanca 36885, MexicoThis study explores children’s emotions through a novel approach of Generative Artificial Intelligence (GenAI) and Facial Muscle Activation (FMA). It examines GenAI’s effectiveness in creating facial images that produce genuine emotional responses in children, alongside FMA’s 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’s 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.https://www.mdpi.com/2504-2289/9/1/15generative artificial intelligencefacial emotion recognitionfacial muscle activationartificial neural networks |
| spellingShingle | Manuel A. Solis-Arrazola Raul E. Sanchez-Yanez Ana M. S. Gonzalez-Acosta Carlos H. Garcia-Capulin Horacio Rostro-Gonzalez Eliciting Emotions: Investigating the Use of Generative AI and Facial Muscle Activation in Children’s Emotional Recognition Big Data and Cognitive Computing generative artificial intelligence facial emotion recognition facial muscle activation artificial neural networks |
| title | Eliciting Emotions: Investigating the Use of Generative AI and Facial Muscle Activation in Children’s Emotional Recognition |
| title_full | Eliciting Emotions: Investigating the Use of Generative AI and Facial Muscle Activation in Children’s Emotional Recognition |
| title_fullStr | Eliciting Emotions: Investigating the Use of Generative AI and Facial Muscle Activation in Children’s Emotional Recognition |
| title_full_unstemmed | Eliciting Emotions: Investigating the Use of Generative AI and Facial Muscle Activation in Children’s Emotional Recognition |
| title_short | Eliciting Emotions: Investigating the Use of Generative AI and Facial Muscle Activation in Children’s Emotional Recognition |
| title_sort | eliciting emotions investigating the use of generative ai and facial muscle activation in children s emotional recognition |
| topic | generative artificial intelligence facial emotion recognition facial muscle activation artificial neural networks |
| url | https://www.mdpi.com/2504-2289/9/1/15 |
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