Published Paper Details:

Accurate age detection has emerged as a useful tool in a constantly growing digital environment, spanning sectors including identity verification, wellness, healthcare, and personalized services. Conventional age estimation techniques use static inputs, but new opportunities for data-driven, real-time estimation have been made possible by developments in machine learning and predictive analytics. By utilizing deep learning models with real-time processing, we aim to create a robust system that adapts to diverse datasets and varying environmental conditions. The proposed framework employs optimization techniques to enhance efficiency of the model, ensuring seamless integration into cloud-based platforms for scalable deployment. The proposed study also indulges in deployment of the age detection model as a Software-as-a-Service for various security-based applications, such as security in net-banking. Beyond technical implementation, this study addresses the ethical considerations surrounding age detection, emphasizing fairness, transparency, and data privacy. The results of this research contribute to the growing field of machine learning-driven security applications, providing more insights, and secure identity verification. Through this work, ChronoDetect aims to demonstrate how AI-driven age estimation can be both efficient and ethically responsible, paving the way for broader applications in digital security domains.