Subsurface scattering deconvolution for improved NIR-visible facial image correlation

Significant improvements in face-recognition performance have recently been achieved by obtaining near infrared (NIR) probe images. We demonstrate that by taking into account the differential effects of sub-surface scattering, correlation between facial images in the visible (VIS) and NIR wavelengths can be significantly improved. Hence, by using Fourier analysis and Gaussian deconvolution with variable thresholds for the scattering deconvolution radius and frequency, sub-surface scattering effects are largely eliminated from perpendicular isomap transformations of the facial images. (Isomap images are obtained via scanning reconstruction, as in our case, or else, more generically, via model fitting). Thus, small-scale features visible in both the VIS and NIR, such as skin-pores and certain classes of skin-mottling, can be equally weighted within the correlation analysis. The method can consequently serves as the basis for more detailed forms of facial comparison.