Cost vs. Performance: Raspberry Pi and ESP32 in Face Detection Tasks

Abstract

A cost-effective, scalable solution that leverages low-cost embedded systems for computationally intensive tasks like face detection is critical for the growing demands of IoT applications, motivating the development of innovative hybrid architectures. This paper proposes such a framework, combining the ESP32 CAM as a distributed image capture unit with light preprocessing and the Raspberry Pi as a centralized processing unit. By addressing the limitations of standalone implementations—low computational capability of the ESP32 CAM and high deployment costs of the Raspberry Pi—our framework achieves a balance between affordability and performance. We present the analysis of the devices, evaluating metrics such as frame rate, detection accuracy, and cost-efficiency. The results highlight the potential of the hybrid system to significantly lower costs while enabling scalable, real-time face detection in IoT scenarios. This study contributes to the ongoing research by proposing an adaptable, resource-optimized framework suitable for diverse use cases, from smart surveillance to retail monitoring, paving the way for more efficient IoT-based vision systems.