在不同的照明条件下估计皮肤色调
Success K Mbatha1, Marthinus J Booysen1,2, Rensu P Theart1
1Department of E&E, Stellenbosch University, Stellenbosch 7602, South Africa.
Journal of imaging
|May 24, 2024
概括
这项研究开发了一个卷积神经网络模型,以准确估计在各种照明条件下皮肤色调. 该模型显示了在不同皮肤色素水平的计算机视觉应用中提高公平性的承诺.
科学领域:
- 计算机视觉 计算机视觉
- 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:
- 准确的皮肤色调估计对于公平和高性能计算机视觉应用,包括医学诊断和面部识别至关重要.
- 照明显著影响人们感知到的皮肤色调,这对在不同照明条件下进行一致分析提出了挑战.
- 现有的方法在人类皮肤色素的全谱中难以准确.
研究的目的:
- 改进和评估一个卷积神经网络 (CNN) 模型,以进行可靠的皮肤色调估计.
- 为了确保模型在不同肤色和各种照明场景中保持一致的准确性.
- 提高利用皮肤色数据的计算机视觉系统的公平性和可靠性.
主要方法:
- 收集了来自志愿者的21,375张图像的数据集,这些图像代表了整个皮肤色素谱.
- 一个卷积神经网络 (CNN) 模型被开发和评估用于皮肤色调估计.
- 蒙克皮肤色调尺度 (10分) 用于分类和评估皮肤色调.
主要成果:
- 一个基于回归的CNN模型表现出卓越的性能,估计目标距离为0.5.5.
- 使用2为估计到目标皮肤色调距离的值,获得了85.45%和97.16%的准确性.
- 该模型在较浅的皮肤色调中显示出很高的准确性,在较深的皮肤色调中显示出中等的准确性,在中等的皮肤色调中显示出较低的准确性.
结论:
- 开发的CNN模型提供了一种可靠的方法,用于在不同照明条件下估计皮肤色调.
- 该模型的性能突出显示了在计算机视觉应用中提高公平性的潜力.
- 进一步的研究可能将重点放在改善中等色调皮肤的准确性和扩大应用范围.
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