使用红外线来提高具有高颜色皮肤的个体的面部识别
Alex G Muthua1, Rensu P Theart1, Marthinus J Booysen1,2
1Department of Electrical and Electronic Engineering, Stellenbosch Universiteit, Stellenbosch, South Africa.
iScience
|July 7, 2023
概括
这项研究通过结合红外 (IR) 频谱成像来增强对较深肤色的面部识别能力. 包括IR数据显著提高了准确性,解决了可见光谱面部识别系统中代表性不足和光吸收问题.
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
- 电气工程 电气工程
背景情况:
- 面部识别系统表现出高颜色皮肤色调的性能限制.
- 这种偏见源于训练数据集中的代表性不足和可见光谱中的光吸收.
- 现有的系统很难仅仅使用可见光来捕捉黑色皮肤的足够细节.
研究的目的:
- 为了提高具有高颜色肤色的个体面部识别的准确性.
- 调查红外 (IR) 频谱在增强面部识别能力方面的实用性.
- 为了比较使用可见光,红外光和全光谱的面部识别系统的性能.
主要方法:
- 增强现有的面部识别数据集,包括具有高颜色皮肤个体的图像.
- 通过可见光,红外光和全光谱捕获的图像.
- 微调现有的面部识别模型和评估的性能指标.
主要成果:
- 纳入红外光谱显著提高了面部识别准确度,从高颜色面部的97.5%提高到99.0%.
- 接收器运行特征 (ROC) 曲线的曲线下的面积 (AUC) 值也显示出明显的改善.
- 面部定向和狭窄的裁剪增强了表现,鼻子区域被确定为关键特征.
结论:
- 红外频谱的整合为具有高颜色皮肤的个体的面部识别准确性提供了显著的改进.
- 解决数据集偏差和利用多光谱成像是开发更公平,更强大的生物识别系统的关键.
- 对最佳特征提取和数据增强策略的进一步研究可以提高不同人群的面部识别性能.
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