Custom Model and Testing Dataset For Age and Gender Detection

Age and gender detection are of huge importance for many applications such as security, marketing, authorization, and medical systems. Voice and fingerprints are two different methods used to identify the age and gender of an individual. However, a widely used and promising way for age and gender detection is using computer vision in combination with deep learning. This research presents an efficient and accurate system that can identify the gender and estimate the age of individuals in real-time using surveillance cameras. We employ computer vision techniques, such as face detection using Haar cascade classifier, and leverage deep learning models trained on large-scale datasets to achieve robust performance. Through rigorous experimentation and optimization, we demonstrate the effectiveness of our approach, and accurately predicting gender and estimating age. The results highlight the potential of smart cameras in enabling intelligent systems that can analyze human behavior and contribute to various domains, including security, marketing, and personalized services. Our findings pave the way for the widespread adoption of smart cameras with gender and age detection capabilities, revolutionizing how we interact with and understand human demographics in real-world scenarios.