基于非接触漫反射光谱的黑色素指数检测研究

Melanin Index Detection by Non-Contact Diffuse Reflection Spectroscopy

黑色素指数是表征皮肤黑色素含量的指标,对黑色素指数进行准确稳定的测量具有重要意义。将非接触式测量装置测量的漫反射光谱与机器学习结合进行了人体皮肤黑色素指数的检测。首先,建立非接触漫反射光谱测量装置并收集数据,分别利用竞争性自适应加权法(CARS)和黑色素指数定义进行数据降维,以证明机器学习在光谱数据降维中的合理性。然后比较了常用机器学习回归模型在预测黑色素指数中的表现,最后选择合适的黑色素指数回归模型。实验结果表明,在基于非接触性皮肤漫反射率光谱的机器学习预测模型中,K近邻回归模型可以准确地获取黑色素指数值,数据验证的决定系数R^(2)达到0.995以上,最小平均绝对误差为1.251。通过比较筛选5波长数据和CARS降维数据的准确性发现,CARS的降维数据不仅可以筛选出皮肤中不同发色团的特征吸收峰,还可以在预测模型中获得相似的预测精度。本研究旨在选择合适的预测模型以提高黑色素的检测精度。

Melanin index is an indicator of the melanin content in the skin.It is important to have an accurate and stable measurement of the melanin index.We utilize a non-contact measurement device to measure diffuse reflectance spectroscopy which combined with machine learning for human skin melanin index detection.First,a non-contact diffuse reflectance spectroscopy measurement device is built and the data is collected.The data is deformed using competitive adaptive reweighted sampling(CARS)and melanin index definitions respectively to prove the rationality of machine learning for spectral data deformation.Then,the performance of machine learning regression models commonly used in predicting melanin indices is compared,and finally a suitable melanin index regression model is selected.The experimental results show that among the machine learning prediction models that combined with the non-contact skin-based diffuse reflectance spectroscopy,the K-nearest neighbor regression model can accurately obtain the melanin index values,the coefficient of determination R^(2) reaching above 0.995 for data validation,and the minimum mean absolute error is 1.251.After comparing the accuracy of five screening wavelength and the dimensionality reduction data obtained by CARS,it is found that the dimensionality reduction data obtained by CARS not only screens out characteristic absorption peaks of different skin chromophore,but also obtains similar prediction accuracy in the prediction models.The aim of this study is to select a suitable prediction model to improve the accuracy of the melanin detection.