Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Ahmad, Akram; Singh, Vaishali; Upreti, Kamal Title A name given to the resource Emotion Recognition Through Facial Expressions: A Machine Learning Perspective in Mobile Multimedia Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Journal of Mobile Multimedia;Volume;21;Issue;1;pp.87-112 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.13052/jmm1550-4646.2114" target="_blank" rel="noreferrer noopener">https://doi.org/10.13052/jmm1550-4646.2114</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105003940451?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003940451?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Ahmad A., Department of Computer Science, Maharishi University of Information Technology, Lucknow, India; Singh V., Department of Computer Science, Maharishi University of Information Technology, Lucknow, India; Upreti K., Christ University, Delhi NCR Campus, Ghaziabad, India Description An account of the resource Facial expression-based emotion detection is very attractive because of the possibilities in security systems, mental health monitoring, and human-computer interaction. Even with the progress in accuracy in real-world settings, issues such as the lack of balanced datasets and the inability to differentiate between faint or superimposed emotions continue to plague it. This study aims to bridge these constraints by developing a CNN-based model that would be able to recognize face emotions reliably and be utilized in real-time situations, such as webcam integration. The Affect Net dataset, which is a comprehensive collection of over a million facial photos labeled with the seven major emotions of anger, disgust, fear, happiness, neutrality, sadness, and surprise, was used to train the proposed model. Other pre-processing data techniques used include grayscale conversion, normalization, scaling, and data supplementation to increase the robustness of the model. Using metrics like accuracy and loss trends for evaluation, the model demonstrated efficiency stability at around the 30th training phase. When the model is compared to existing models, this proposed model can attain the competitive level of accuracy up to approximately 60%. It also has the potential to run in real applications through its webcam integration. While the model can differentiate between various clear-cut emotions, it becomes ineffective at identifying subtle emotions, which include Fear and Neutral majorly because of unbalanced data and the subtleness of these expressions. 2025 River Publishers. Subject The topic of the resource advanced machine learning; Emotion detection; facial expressions; feature extraction; human-computer interaction Publisher An entity responsible for making the resource available River Publishers Relation A related resource ISSN: 15504646; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online