Smart Surveillance: CNN-Based Age and Gender Detection

In the twenty-first century, humanity has reached a stage of remarkable technological advancements, leading to groundbreaking achievements. The rise of social media and online platforms has significantly increased the demand for automatic age and gender classification across various applications. While substantial research has been conducted in this domain, there remains considerable scope for enhancement, especially in age classification. This study aims to compare the performance of Convolutional Neural Networks (CNNs) with Support Vector Machines (SVMs) on the same dataset, challenging the prevailing notion that CNNs are inherently superior for image classification tasks. However, existing approaches using real-world images still fall short of achieving the desired accuracy, particularly when compared to the significant advancements seen in related areas like age estimation. In this paper, we demonstrate how deep CNNs can effectively learn feature representations to improve classification accuracy. We introduce a simplified CNN architecture that requires minimal training data while delivering high performance. The results reveal that our approach significantly surpasses current benchmarks for age and gender estimation.