Abstract
This paper investigates the feasibility of multi-task learning (MTL) for facial recognition on the Raspberry Pi, a low-cost single-board computer, demonstrating its ability to perform complex deep learning tasks in real time. Using MobileNet, MobileNetV2, and InceptionV3 as base models, we trained MTL models on a custom database derived from the VGGFace2 dataset, focusing on three tasks: person identification, age estimation, and ethnicity prediction. MobileNet achieved the highest accuracy, with 99% in person identification, 99.3% in age estimation, and 99.5% in ethnicity prediction. Compared to previous studies, which primarily relied on high-end hardware for MTL in facial recognition, this work uniquely demonstrates the successful deployment of efficient MTL models on resource-constrained devices like the Raspberry Pi. This advancement significantly reduces computational load and energy consumption while maintaining high accuracy, making facial recognition systems more accessible and practical for real-world applications such as security, personalized customer experiences, and demographic analytics. This study opens new avenues for innovation in resource-efficient deep learning systems.