Research Information System
7th INTERNATIONAL ANTALYA CONGRESS OF SCIENTIFIC RESEARCH AND INNOVATIVE STUDIES , Antalya, Turkey, 11 - 13 May 2024, vol.1, pp.103-116
This paper uses the Raspberry Pi, a popular single-board computer to investigate an approach
to multi-task learning for face recognition. The paper aims to demonstrate how this low-cost
platform can execute complex deep-learning tasks in real-time. To accomplish this, we
utilized MobileNet, MobileNetV2, and InceptionV3 as the core models for shared layers,
owing to their balance of efficiency and accuracy. The training was conducted on the
VGGFace2 dataset, a widely recognized source of facial images for machine-learning
applications. The multi-task learning approach enabled the simultaneous execution of three
tasks: identifying individuals, estimating their ages, and predicting their races. The system
demonstrated impressive accuracy across all tasks by leveraging shared layers among these
deep learning models. Testing results were exceptional, with 97% accuracy for identifying
people, 97% for age estimation, and 98% for ethnicity prediction. These high success rates
suggest that Raspberry Pi-based face recognition systems have significant potential in realworld applications, such as security systems, personalized customer experiences, and
demographic data analysis. The study concludes by emphasizing the significance of multitask learning on compact hardware. The high accuracy rates achieved indicate that complex
deep learning models can operate efficiently in a resource-constrained environment. This
finding opens the door to further innovation, allowing developers to create more adaptable
and accessible face recognition solutions. The multi-task approach also suggests a path
towards more efficient resource usage, potentially reducing the overall computational load
and energy consumption in real-time applications. Overall, the paper provides a compelling
case for implementing multi-task learning for face recognition on Raspberry Pi, with
impressive accuracy and versatility. This work points towards future opportunities for
extending this approach to other low-cost platforms and multi-functional applications, driving
innovation in edge computing