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 Conference Paper Faculty Publications- Conference Papers 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 Chaudhary, Anubhav; Poonia, Ramesh Chandra; Shanbhog, Manjula Title A name given to the resource MobileNetV3-Based Fine-Tuned Facial Emotion Recognition with Targeted Class Balancing 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 Proceedings of the 2025 International Conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2025;pp.1574-1579 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1109/AISummit66170.2025.11411128" target="_blank" rel="noreferrer noopener">https://doi.org/10.1109/AISummit66170.2025.11411128</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105035308851?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105035308851?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 Chaudhary A., Christ University, Department of Computer Science, Bangalore, India; Poonia R.C., Christ University, Department of Computer Science, Bangalore, India; Shanbhog M., Christ University, Department of Computer Science, Bangalore, India Description An account of the resource Facial emotion recognition (FER) is a pillar of affective computing and augmented human computer interaction, but has been stymied by the problem of class imbalance and lack of prevalence of subtle emotional differences. This paper presents a lightweight FER framework based on the MobileNetV3 architecture with a fine-tuned and weighted dataset that applies class balance and class weighting as strategies that optimized the three-class classification of three discrete emotions Angry, Happy, and Sad. The characteristics of the dataset were assembled comprising a total of 7,305 labelled facial images, based on the KDEF, Kaggle, and Face Expression Dataset hence inheriting the heterogeneity of subjects and imaging conditions. The pre-processing of all of the images carried out as the RGB input and after resizing (224 x 224 pixels) a massive data augmentation done to encourage generalization. Transfer learning in the training pipeline is done through progressive unfreezing and the weight of the loss on the minority classes (Angry and Sad) are boosted to improve the performance of detection. The achieved model resulted in an accuracy of 87% on the test set, and had equal accuracy in preciseness, recall, and F1-scores over all emotion types. Extended error analysis revealed that the majority of cases that were misclassified fell between the categories Angry and Sad because they were mistaken due to combining visual cues. Even then, the performance showed stability despite the variable lighting as well as in variable positional context. In Comparison, MobileNetV3 outperforms state-of-art-lightweight models with respect to accuracy and computation of similar computational complexity. 2025 IEEE. Subject The topic of the resource Convolutional Neural Network; Facial Expression Recognition; MobileNetV3; Transfer Learning; VGG16 Publisher An entity responsible for making the resource available Institute of Electrical and Electronics Engineers Inc. Relation A related resource ISBN: 979-833155379-1; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online