安全有效地实施面部情绪检测,用于智能患者监控系统
Kh Shahriya Zaman1, Md Mamun Bin Ibne Reaz1
1Department of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia (UKM) Bangi 43600 Malaysia.
Quantitative biology (Beijing, China)
|February 12, 2026
概括
我们使用低功耗FPGA开发了一种高效的设备面部情绪识别 (FER) 系统. 这种方法通过近似卷积神经网络 (CNN) 重量来减少计算资源,提高敏感生物医学应用程序的隐私.
科学领域:
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 面部表情识别 (FER) 对于智能监测和了解患者精神状态至关重要.
- 当前高精度的FER算法需要大量的计算资源,这导致基于云的处理带来隐私风险.
- 尽量减少数据传输是安全和高效的设备上FER的关键.
研究的目的:
- 通过使用低功耗FPGA提出一个高效的设备上FER系统.
- 在资源有限的设备上减少FER算法的计算复杂性.
- 通过本地处理敏感信息来增强数据隐私.
主要方法:
- 在现场可编程门阵列 (FPGA) 平台上实现了一个卷积神经网络 (CNN) 算法.
- 编码的CNN权重以近似的签名数字来减少多重积累 (MAC) 操作.
- 对预训练的MobileNet-v2和ResNet18模型的应用重量近似.
主要成果:
- 与整数加权模型相比,FPGA资源需求至少减少了22%.
- 在分类准确性方面显示的微不足道的损失.
- 在已建立的CNN架构上验证了近似方法的效率.
结论:
- 开发的系统可以实现安全和低功耗的设备上FER.
- 接近方法可以扩展到其他基于图像的生物医学研究领域.
- 这项研究有助于创建保护隐私的医疗保健人工智能.
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