Child safety system using HAAR and CNN Algorithm

Concern over children's safety grown rapidly. both public and private setups have Using HAAR Cascade classifiers and Convolutional Neural Networks (CNNs), this project suggests an intelligent child safety system that uses computer vision techniques to detect, monitor, and alert in real time. For quick and effective face detection, the scheme comprises HAAR Cascade, which is particularly well-suited for environments with limited resources, such as embedded systems. The accuracy and dependability of child identification are improved by using a CNN-based model to classify and identify whether the detected subject is a registered child or an unidentified person after faces have been detected. The 60% of the participants can be used to track children's location and sound an alert when one is located in parks, schools, shopping centers, and smart home settings.