AUTHOR=Gonzalez-Acosta Ana M. S. , Vargas-Treviño Marciano , Batres-Mendoza Patricia , Guerra-Hernandez Erick I. , Gutierrez-Gutierrez Jaime , Cano-Perez Jose L. , Solis-Arrazola Manuel A. , Rostro-Gonzalez Horacio 

TITLE=The first look: a biometric analysis of emotion recognition using key facial features

JOURNAL=Frontiers in Computer Science

VOLUME=Volume 7 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/computer-science/articles/10.3389/fcomp.2025.1554320

DOI=10.3389/fcomp.2025.1554320

ISSN=2624-9898

ABSTRACT=IntroductionFacial 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.MethodsA 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.ResultsThe 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.DiscussionThe 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.