Prediction of skin ages by means of multi-spectral light sources

Assessment of skin aging is a complex biological process that depends on various internal and external factors but has become important due to personalized skin health and cosmetic treatments. Although there are a small number of attempts to assess the skin aging, they identify only one of the previously classified skin aging groups. The methods used to achieve it are also based generally on highly expensive measurement devices. This work therefore proposes novel low-cost skin aging assessment apparatus by using light back-scatter intensity level of Red, Blue, Green and Infrared bands. This is further enhanced by using a machine learning method to accurately predict actual skin age. The proposed method appears to be highly capable of capturing multi-layer cellular changes exhibited by the biological skin aging process and predicting skin ages with a root-mean-square error of as low as 0.160 by using only four features based on the four multi-spectral light sources. This assessment kit seems to be the first of its kind, which is expected to bring great benefit to both personalized skin healthcare and cosmetic sectors.