Publication Type : Conference Proceedings
Publisher : IEEE
Source : 2024 5th International Conference on Innovative Trends in Information Technology (ICITIIT)
Url : https://doi.org/10.1109/icitiit61487.2024.10580121
Campus : Nagercoil
School : School of Computing
Year : 2024
Abstract : Recognizing age, gender, and ethnicity is a critical issue in computer vision with several real-world applications, such as social analytics, surveillance and customized user experiences. This paper suggests a method based on deep learning for reliable multimodal gender, ethnicity and age recognition from facial information. This framework focuses on convolutional neural networks (VGG16) to extract discriminative features at different scales, while handling temporal dependencies. This study introduces novel attention mechanisms to highlight salient facial regions for improved recognition performance. Our approach has been extensively tested on benchmark datasets, showcasing its remarkable performance by achieving cutting-edge results in accuracy, robustness, and computational efficiency. It provides 82% accuracy in VGG16 for Age recognition, 95.31% accuracy in VGG16 for Gender recognition, 98.44% accuracy in VGG16 for Race recognition.
Cite this Research Publication : M Ragul, S. Veluchamy, Deep Transfer Learning Empowered Facial Features based Age,Gender and Ethnicity Prediction System, 2024 5th International Conference on Innovative Trends in Information Technology (ICITIIT), IEEE, 2024, https://doi.org/10.1109/icitiit61487.2024.10580121