Does Face Image Statistics Predict a Preferred Spatial Frequency for Human Face Processing?

TL;DR

This study investigates the relationship between face image statistics and human face processing preferences, revealing that face spectra have distinct amplitude characteristics aligned with recognition importance.

Contribution

It provides the first evidence linking face image spectral properties to the preferred spatial frequencies in human face recognition.

Findings

Face spectra fall off more steeply than natural images.

Whitening spectra highlights frequencies important for face identity.

Results support stimulus properties influence face processing preferences.

Abstract

Psychophysical experiments suggested a relative importance of a narrow band of spatial frequencies for recognition of face identity in humans. There exists, however, no conclusive evidence of why it is that such frequencies are preferred. To address this question, I examined the amplitude spectra of a large number of face images, and observed that face spectra generally fall off steeper with spatial frequency compared to ordinary natural images. When external face features (like hair) are suppressed, then whitening of the corresponding mean amplitude spectra revealed higher response amplitudes at those spatial frequencies which are deemed important for processing face identity. The results presented here therefore provide support for that face processing characteristics match corresponding stimulus properties.