CPU Performance Analysis of Running a Face Recognition Algorithm on Raspberry Pi Using Machine Learning

Embedded systems such as Raspberry Pi have gained popularity due to their small size, low cost, and ability to run various applications, including machine learning algorithms. Various studies have investigated the performance of Raspberry Pi for machine learning tasks, including image classification and object detection. These studies indicate that Raspberry Pi can serve as a viable platform for running machine learning algorithms. In this study, we review the current state of research on the CPU performance of running face recognition algorithms on Raspberry Pi. This research presents the testing done on a facial recognition algorithm using Machine Learning to reach the determination value needed for a real-time facial recognition-based software to decide the pass-fail criteria for a familiar face in a database of choice. This paper presents the results and analysis of the timing constraints of running a Machine Learning algorithm on a Raspberry Pi 3 Model B.