通过Raspberry Pi上的多任务学习实时面部识别
Abdulatif Ahmed Ali Aboluhom1, Ismet Kandilli2
1Engineering Faculty, Electronics Department, Ibb University, Ibb, Yemen. abdullatif1995.11@gmail.com.
Scientific reports
|August 4, 2025
概括
这项研究表明,面部识别的高效多任务学习 (MTL) 在Raspberry Pi上是可行的. 在这种低成本的设备上,MobileNet实现了对人身识别,年龄和种族预测的高准确性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 嵌入式系统 嵌入式系统
背景情况:
- 面部识别通常需要高端硬件,限制其可访问性.
- 多任务学习 (MTL) 提供了效率,但通常依赖于强大的计算资源.
- 像Raspberry Pi这样的资源有限的设备对复杂的深度学习任务提出了挑战.
研究的目的:
- 调查在树Pi上部署高效的MTL面部识别的可行性.
- 在本设备上评估不同基准模型 (MobileNet,MobileNetV2,InceptionV3) 对MTL任务的性能.
- 在低成本的嵌入式硬件上演示实时深度学习能力.
主要方法:
- 使用MobileNet,MobileNetV2和InceptionV3作为基础架构进行训练的MTL模型.
- 使用自定义数据库,该数据库来源于VGGFace2数据集.
- 专注于三个面部识别任务:人身份识别,年龄估计和种族预测.
主要成果:
- 移动网络实现了卓越的准确性:99%的人身份识别,99.3%的年龄估计和99.5%的种族预测.
- 在Raspberry Pi上展示了MTL模型的成功实时性能.
- 实现了与高端硬件上的系统可比的高精度.
结论:
- 有效的MTL模型可以成功地部署在资源有限的设备上,如用于面部识别的Raspberry Pi.
- 这种方法显著降低了计算负载和能源消耗.
- 面部识别系统变得更加易于使用,对现实世界的应用更加实用.
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