Enhancing age and gender verification in OTT accounts using deep learning techniques.

Protecting children from inappropriate audio-visual content requires making sure they are exposed to age and gender-appropriate information. This research presents an innovative method for ascertaining user age in OTT (Over-The-Top) accounts with a tailored convolutional neural network (CNN) model, aimed at limiting access to inappropriate content. The proposed method aims to accurately evaluate the age appropriateness of content and restrict access to information that is not suitable for children, addressing the issue of children often utilizing their parents' accounts. OTT platforms enhance their responsibilities by providing suitable content to customers through age identification, so creating a safer and more secure digital environment for all users. This solution not only promotes responsible content consumption among minors but also reduces parental concerns regarding their children's OTT usage. The proposed model provides better accuracy of 91% for UTK Face dataset for the age and gender identification. A user interface (UI) for age verification is developed using OpenCV and Flask Framework. By addressing the crucial issue of child safety, prohibiting minors from accessing unsuitable information, and promoting responsible content consumption, the proposed solution establishes a new benchmark for OTT platforms.