Real-time facial recognition via multitask learning on raspberry Pi
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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-97490-6 |
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| author | Abdulatif Ahmed Ali Aboluhom Ismet Kandilli |
| author_facet | Abdulatif Ahmed Ali Aboluhom Ismet Kandilli |
| author_sort | Abdulatif Ahmed Ali Aboluhom |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-78231ee2e64c43039aa56c4f3faaefe6 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-78231ee2e64c43039aa56c4f3faaefe62025-08-20T04:02:45ZengNature PortfolioScientific Reports2045-23222025-08-0115111510.1038/s41598-025-97490-6Real-time facial recognition via multitask learning on raspberry PiAbdulatif Ahmed Ali Aboluhom0Ismet Kandilli1Engineering Faculty, Electronics Department, Ibb UniversityElectronics and Automation Department, Kocaeli UniversityAbstract 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.https://doi.org/10.1038/s41598-025-97490-6Multi-task learningRaspberry PiDeep learningFace recognitionReal-time |
| spellingShingle | Abdulatif Ahmed Ali Aboluhom Ismet Kandilli Real-time facial recognition via multitask learning on raspberry Pi Scientific Reports Multi-task learning Raspberry Pi Deep learning Face recognition Real-time |
| title | Real-time facial recognition via multitask learning on raspberry Pi |
| title_full | Real-time facial recognition via multitask learning on raspberry Pi |
| title_fullStr | Real-time facial recognition via multitask learning on raspberry Pi |
| title_full_unstemmed | Real-time facial recognition via multitask learning on raspberry Pi |
| title_short | Real-time facial recognition via multitask learning on raspberry Pi |
| title_sort | real time facial recognition via multitask learning on raspberry pi |
| topic | Multi-task learning Raspberry Pi Deep learning Face recognition Real-time |
| url | https://doi.org/10.1038/s41598-025-97490-6 |
| work_keys_str_mv | AT abdulatifahmedaliaboluhom realtimefacialrecognitionviamultitasklearningonraspberrypi AT ismetkandilli realtimefacialrecognitionviamultitasklearningonraspberrypi |