Face recognition using deep learning on Raspberry Pi

Abstract

Abstract Facial recognition on resource-limited devices such as the Raspberry Pi poses a challenge due to inherent processing limitations. For real-time applications, finding efficient and reliable solutions is critical. This study investigated the feasibility of using transfer learning for facial recognition tasks on the Raspberry Pi and evaluated transfer learning that leverages knowledge from previously trained models. We compared two well-known deep learning (DL) architectures, InceptionV3 and MobileNetV2, adapted to face recognition datasets. MobileNetV2 outperformed InceptionV3, achieving a training accuracy of 98.20% and an F1 score of 98%, compared to InceptionV3’s training accuracy of 86.80% and an F1 score of 91%. As a result, MobileNetV2 emerges as a more powerful architecture for facial recognition tasks on the Raspberry Pi when integrated with transfer learning. These results point to a promising direction for deploying efficient DL applications on edge devices, reducing latency, and enabling real-time processing.