Face recognition using deep learning on Raspberry Pi

作     者：Aboluhom, Abdulatif Ahmed Ali Kandilli, Ismet 

作者机构：Kocaeli Univ Engn Fac Elect & Commun Dept TR-41380 Kocaeli Turkiye Kocaeli Univ Elect & Commun Dept TR-41380 Kocaeli Turkiye 

出 版 物：《COMPUTER JOURNAL》 (Comput J)

年 卷 期：2024年第67卷第10期

页      面：3020-3030页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：face recognition deep learning transfer learning Raspberry Pi 

摘      要：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.