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Age Group Classification and Gender Prediction using Facial Skin Texture Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 186 - Number 53 |
| Year of Publication: 2024 |
| Authors: Logeswari Saranya R., Umamaheswari K. |
10.5120/ijca2024924208
|
Logeswari Saranya R., Umamaheswari K. . Age Group Classification and Gender Prediction using Facial Skin Texture Analysis. International Journal of Computer Applications. 186, 53 ( Dec 2024), 20-26. DOI=10.5120/ijca2024924208
Abstract
In today's rapidly evolving world, facial recognition technology plays a pivotal role. The extraction of human attributes such as gender and age from biometric data has gained significant attention in recent years. Despite advances in computer vision, accurately predicting age and gender from unprocessed, live facial images remains a challenge in commercial and real-world applications. Current age classification methods rely on texture and shape information, often without considering filtered facial features. This research uses Convolutional Neural Network for age group and gender estimation using filtered images, which may affect perceived age. By comparing original and filtered images, the true age of individuals can be determined while maintaining their identity. Age groups are dynamically categorized based on the number of groups using a deep convolutional neural network. Experimental results demonstrate high accuracy in both age and gender prediction, outperforming existing techniques and contributing to a more robust security system.
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