Living-Skin Detection based on Spatio-Temporal Analysis of Structured Light Pattern

Living-skin detection is an important step for imaging photoplethysmography and biometric anti-spoofing. In this paper, we propose a new approach that exploits spatio-temporal characteristics of structured light patterns projected on the skin surface for living-skin detection. We observed that due to the interactions between laser photons and tissues inside a multi-layer skin structure, the frequency-domain sharpness feature of laser spots on skin and non-skin surfaces exhibits clear difference. Additionally, the subtle physiological motion of living-skin causes laser interference, leading to brightness fluctuations of laser spots projected on the skin surface. Based on these two observations, we designed a new living-skin detection algorithm to distinguish skin from non-skin using spatio-temporal features of structured laser spots. Experiments in the dark chamber and Neonatal Intensive Care Unit (NICU) demonstrated that the proposed setup and method performed well, achieving a precision of 85.32%, recall of 83.87%, and F1-score of 83.03% averaged over these two scenes. Compared to the approach that only leverages the property of multilayer skin structure, the hybrid approach obtains an averaged improvement of 8.18% in precision, 3.93% in recall, and 8.64% in F1-score. These results validate the efficacy of using frequency domain sharpness and brightness fluctuations to augment the features of living-skin tissues irradiated by structured light, providing a solid basis for structured light based physiological imaging. Our code is available at: https://github.com/contactless-healthcare/Structured-light-based-Living-skin-Detection