Investigating the relationship between chronic stress and facial features at distinct wavelengths using independent component analysis

Abstract This study presents a novel approach for objective chronic stress assessment using multi-wavelength facial imagery and independent component analysis (ICA). We investigated the relationship between facial images captured across distinct spectral bands (infrared, near-infrared (780–900 nm, 900–1700 nm), and visible ( L *, a *, b *)) and participants’ chronic stress scores. Applying ICA, we identified statistically independent facial features and found significant correlations between their component weights and chronic stress levels. Notably, visible and infrared wavelengths exhibited robust and more generalized correlations across participants compared to near-infrared. Our analysis revealed stress-correlated features predominantly in the perioral and periorbital regions, intriguing given their association with acute stress responses, yet suggesting distinct underlying physiological adaptations for chronic stress. This ICA-based method offers an interpretable and data-driven framework for uncovering subtle, persistent manifestations of chronic stress in the face, paving the way for scalable non-contact health monitoring solutions.