Emotion Based Age and Gender Detection Using Ml
Year: 2025
Type: article
Abstract: ABSTRACTIn recent years, emotion-based gender and age detection using machine learning has gained significantnotice due to its potential use case of it in various domains, along with marketing, security, and humancomputerinteraction. This study explores the effectiveness of convolutional neural networks (CNNs)in accurately classifying gender and age based on human facial expressions. Specifically, we leverageadvanced architectures such as VGG16 and ResNet, which are renowned for their deep learningcapabilities. By utilizing transfer learning techniques, we enhance the feature extraction process fromfacial images, thereby improving the effectiveness of model performance on age and genderclassification tasks. Our experiments demonstrate that integrating emotional analysis withdemographic detection can yield more personalized and context-aware interactions. We present acomparative analysis of the performance of VGG16 and ResNet models in this domain. The resultsindicate that while both models achieve commendable accuracy, ResNet's residual connectionssignificantly mitigate the vanishing gradient problem, leading to superior performance in recognizingnuanced emotional expressions across different age groups and genders. Additionally, we discuss theimplications of these findings in real-world applications, emphasizing the importance of ethical considerations when deploying emotion detection systems. This study aims to provides the growingstructure of research in affective computing and provides a foundation for future advancements inemotion-based demographic detection technologies. Keywords: Emotion detection, Age classification, Gender classification, Machine learning,Convolutional neural networks, VGG16, ResNet, Transfer learning, Affective computing.
Authors: Mr.Mayur Vanve, Mr.Ritesh Rayate, Mr.Shreyas Shete, Mr.Sahil Tarle, Ms.Ashwini Gaikwad, Mr.Mahesh Bhandakkar
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6949503818
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