The first look: a biometric analysis of emotion recognition using key facial features
Introduction: Facial expressions play a crucial role in human emotion recognition and social interaction. Prior research has highlighted the significance of the eyes and mouth in identifying emotions; however, limited studies have validated these claims using robust biometric evidence. This study in...
| Autores: | , , , , , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Ramon Llull (URL) |
| Repositorio: | DAU Arxiu Digital de la Universitat Ramon Llull |
| OAI Identifier: | oai:dau.url.edu:20.500.14342/5248 |
| Acceso en línea: | http://hdl.handle.net/20.500.14342/5248 https://doi.org/10.3389/fcomp.2025.1554320 |
| Access Level: | acceso abierto |
| Palabra clave: | Emotion recognition Eye-tracking analysis Facial landmarks Biometric validation Machine learning and AI Emocions Seguiment de la mirada Expressió facial Identificació biomètrica Aprenentatge automàtic Intel·ligència artificial 004 159.9 |
| Sumario: | Introduction: Facial expressions play a crucial role in human emotion recognition and social interaction. Prior research has highlighted the significance of the eyes and mouth in identifying emotions; however, limited studies have validated these claims using robust biometric evidence. This study investigates the prioritization of facial features during emotion recognition and introduces an optimized approach to landmark-based analysis, enhancing efficiency without compromising accuracy. Methods: A total of 30 participants were recruited to evaluate images depicting six emotions: anger, disgust, fear, neutrality, sadness, and happiness. Eye-tracking technology was utilized to record gaze patterns, identifying the specific facial regions participants focused on during emotion recognition. The collected data informed the development of a streamlined facial landmark model, reducing the complexity of traditional approaches while preserving essential information. Results: The findings confirmed a consistent prioritization of the eyes and mouth, with minimal attention allocated to other facial areas. Leveraging these insights, we designed a reduced landmark model that minimizes the conventional 68-point structure to just 24 critical points, maintaining recognition accuracy while significantly improving processing speed. Discussion: The proposed model was evaluated using multiple classifiers, including Multi-Layer Perceptron (MLP), Random Decision Forest (RDF), and Support Vector Machine (SVM), demonstrating its robustness across various machine learning approaches. The optimized landmark selection reduces computational costs and enhances real-time emotion recognition applications. These results suggest that focusing on key facial features can improve the efficiency of biometric-based emotion recognition systems without sacrificing accuracy. |
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