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 Vivek, R.; Sambandam, Rakoth Kandan Title A name given to the resource Hierarchical Demographic Classification Using Multi-output CNN Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Lecture Notes in Networks and Systems;Volume;1673 LNNS;pp.1-16 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-95-3495-1_1" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-95-3495-1_1</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105029660928?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105029660928?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 Vivek R., Department of Computer Science and Engineering, Christ University, Karnataka, Bangalore, India; Sambandam R.K., Department of Computer Science and Engineering, Christ University, Karnataka, Bangalore, India Description An account of the resource This work constructs a multi-output Convolutional Neural Network (CNN) for demographic classification, including age estimation, gender determination, race categorization, and geographical subcategories of South Asia, North Asia, Europe, and Africa. Sub-region classification is further carried out in the Indian subcontinent: Karnataka, Kerala, Tamil Nadu, Punjabi and Kashmiri. The proposed CNN is trained using a heterogeneous dataset and its performance is compared with some robust pre-trained models such as VGG16, ResNet50, InceptionV3, and DenseNet121. The proposed custom CNN architecture makes use of convolutional layers for feature extraction, dropout for regularization, and custom loss weights for balanced multi-task learning. Experimental results are showing high accuracy values of 97% for age estimation, 99.55% for gender classification, and 98.4% for region and sub-region classification. The performance of the model gives a sign of its ability to handle demographic diversity while striving to minimize bias compared to its currently existing solutions. This work provides a foundation for future advancements in hierarchical facial recognition and demographic classification with an emphasis on ethical AI and fairness in real-world applications, including overcoming dataset limitations, exploring hybrid models, and incorporating multimodal data to give more inclusivity and precision. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Demographic classification; Ethnicity prediction; Hierarchical facial recognition; Multi output prediction Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 23673370; ISBN: 978-981953494-4; 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