Human skin phototype and apparent age classification based on machine learning methods of autofluorescence and diffuse reflectance spectroscopic data acquired in vivo

The aim of the current study is to evaluate the classification accuracy and provide corresponding biological interpretation of four classification methods used on autofluorescence (AF) and diffuse reflectance (DR) spectra acquired in vivo on healthy human skin of different phototypes, civil and apparent age groups. Spectroscopic data were acquired on 91 patients using the SpectroLive device. The latter spatially and spectrally-resolved device features four source-to-detector distances (D1-D4) and six excitation light sources: 5 peaks for AF and one broadband white light for DR. For all patients, spectra were acquired on two healthy skin sites i.e. hand palm and inner wrist chosen for their low sun exposure. Four classification methods were tested: Support Vector Machine, K-Nearest Neighbors, Linear Discriminant Analysis and Artificial Neural Network. All combinations of excitation wavelengths, distances and skin sites acquisition were tested to find out the best classification results following a training step on 67 % of the dataset and a validation step on 33 % of the dataset. Classification accuracies were compared using Principal Components Analysis and statistical features. For civil and biological skin age groups discrimination, best classification results (70 % and 76 % respectively) were obtained when combining autofluorescence spectral features from three excitation wavelengths ( 385, 395 and 405 nm) all acquired at the shortest distance (400 mu m) on hand palm. The combination of AF, inner wrist and the longest distance (1 mm) gave the best classification results (76 %) for phototype groups discrimination.