Human Recognition System Using Machine Learning

Technologies for human authentication are essential to intelligent systems, automated processes, and security. An Arduino PIR (Passive Infrared) sensor combined with machine learning is used in this project’s human recognition system to improve accuracy in differentiating motion sources from human presence. Changes in infrared radiation, which indicate movement, are picked up by the PIR sensor. Machine learning algorithms are then used to interpret the data and determine whether the motion is human. An Arduino microcontroller is used to create the system; it receives signals from PIR sensors and sends information to a computer-based machine learning algorithm. To increase detection accuracy, the model examines motion patterns and ambient variables. To train the system using real-world data, a number of algorithms are investigated, such as Support Vector Machines (SVM), Logistic Regression, and Neural Networks. When the system detects a human being, it can initiate pre-programmed functions like turning on lights, giving out alarms, or recording the incident for safety reasons.