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 Harishwaran, S.; Sambandam, Rakoth Kandan; Gokulapriya, R. Title A name given to the resource Gender and Ethnicity Recognition System Based on Convolutional Neural Networks 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 Communications in Computer and Information Science;Volume;2126;pp.66-76 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-031-80842-5_6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-80842-5_6</a> <br /><br /><a href="https://www.scopus.com/pages/publications/85218444670?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/85218444670?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 Harishwaran S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, India; Sambandam R.K., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, India; Gokulapriya R., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Kengeri Campus, Karnataka, Bengaluru, India Description An account of the resource The classification of Gender and Ethnicity has been utilized in diverse scenarios, specifically in the realm of human-computer interaction, visual surveillance, and electronic customer services. Predicting the gender and ethnicity of individuals presents a significant obstacle due to its complex characteristics. The escalating prevalence of social media has emphasized the utmost importance of independently predicting gender and race. In this research endeavor, a framework is utilized which utilizes a Convolutional Neural Network to forecast gender and ethnicity by utilizing various outputs starting from the initial stage. The models performance was evaluated using different metrics, including the F1-score, accuracy, precision, recall, and accuracy. The methodology is evaluated using the UTKFace dataset for predicting gender and ethnicity, and compared the model with previous study to understand which model is giving better accuracy. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. Subject The topic of the resource Convolutional Neural Network; Ethnicity; Gender Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18650929; ISBN: 978-303180841-8; 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