The invention relates to the technical field of
image processing, and discloses a facial health state assessment method and
system based on layered
optical modeling. The objective of the invention is to solve the problems of poor face health state evaluation precision and robustness caused by rough
optical modeling, mixed reflection components, weak environmental adaptability and insufficient multi-dimensional
health index fusion capability in the prior art. The method comprises the following steps: synchronously acquiring a user face
video sequence comprising at least two different
spectral bands through a multispectral imaging device; constructing a multilayer optical transmission model of the
skin tissue, and separating a
specular reflection component and a
diffuse reflection component from the face
video sequence; extracting a physiological
time sequence signal related to subcutaneous
blood flow activity from the separated
diffuse reflection component, and calculating
heart rate,
heart rate variability and blood
oxygen saturation parameters; and fusing the physiological parameters and expression and micro-expression features extracted from the facial
video sequence, and outputting quantitative evaluation results of fatigue degree,
pressure level and emotional state through a pre-trained deep neural
network model. The
system comprises a
multispectral image acquisition module, a layered
optical modeling module, a reflection
component separation module, a physiological parameter inversion module and a health state evaluation module. According to the technical scheme, the
signal-to-
noise ratio and stability of physiological
signal extraction in a complex illumination environment can be remarkably improved, the adaptability to individuals with different
skin colors and dynamic illumination conditions is enhanced, and more accurate and more robust judgment of the high-order health state is achieved.