Real-time Performance Comparison of Face Detection Algorithms using Raspberry Pi

Abstract

Abstract

This study reviewed state-of-the-art face-detection techniques like Haar cascade, Dlib HOG, MTCNN, and MediaPipe; implemented and tested them on Raspberry Pi, and evaluated their accuracy, speed, and frames per second. Overall, the research underscores the practical challenges of face detection, including varying lighting, facial expressions, occlusions, poses, scale of face, and accessories, and provides valuable insights for developers and researchers working on edge AI applications on low-cost edge devices. The study found that the MediaPipe face detection algorithm demonstrated robust performance, even with low-quality images, and showed good efficiency and resource management on the Raspberry Pi. The research emphasized the importance of considering factors like accuracy, speed, and resource efficiency in face detection on edge devices. The findings suggest that MediaPipe is a strong candidate for applications requiring efficient face detection, affordable and versatile platforms like Raspberry Pi. By focusing on the Raspberry Pi, the study offers a unique perspective on the performance of state-of-the-art face detection algorithms in real-world, resource-constrained environments, making it a significant contribution to the field of face recognition and edge computing.