{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T16:46:04Z","timestamp":1783097164266,"version":"3.54.6"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T00:00:00Z","timestamp":1742342400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Comput. Sci."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcomp.2025.1554320","type":"journal-article","created":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T13:34:25Z","timestamp":1742391265000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["The first look: a biometric analysis of emotion recognition using key facial features"],"prefix":"10.3389","volume":"7","author":[{"given":"Ana M. 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