Human and machine recognition of Fourier-Bessel filtered face images

Motivated by a recently proposed biologically-inspired face recognition approach, psychophysical experiments have been carried out. We measured recognition performance of polar frequency filtered face images using an 8-alternatives forced-choice method. Test stimuli were generated by converting the images from the spatial to the polar frequency domain using the Fourier-Bessel transformation (FBT), filtering of the resulting coefficients with band-pass filters, and finally taking the inverse FBT of the filtered coefficients. We also evaluated an automatic FBT-based face recognition model. Contrast sensitivity functions of the human observers peaked in the 8-11.3 radial and angular frequency range, with higher peak sensitivity in the former case. The automatic face recognition algorithm presented similar behavior. These results suggest that polar frequency components could be used by the human face processing system and that human performance can be constrained by the polar frequency information content