7 research outputs found
Preferred Spatial Frequencies for Human Face Processing Are Associated with Optimal Class Discrimination in the Machine
Psychophysical studies suggest that humans preferentially use a narrow band of low spatial frequencies for face recognition. Here we asked whether artificial face recognition systems have an improved recognition performance at the same spatial frequencies as humans. To this end, we estimated recognition performance over a large database of face images by computing three discriminability measures: Fisher Linear Discriminant Analysis, Non-Parametric Discriminant Analysis, and Mutual Information. In order to address frequency dependence, discriminabilities were measured as a function of (filtered) image size. All three measures revealed a maximum at the same image sizes, where the spatial frequency content corresponds to the psychophysical found frequencies. Our results therefore support the notion that the critical band of spatial frequencies for face recognition in humans and machines follows from inherent properties of face images, and that the use of these frequencies is associated with optimal face recognition performance
“I Look in Your Eyes, Honey”: Internal Face Features Induce Spatial Frequency Preference for Human Face Processing
Numerous psychophysical experiments found that humans preferably rely on a narrow
band of spatial frequencies for recognition of face identity. A recently
conducted theoretical study by the author suggests that this frequency
preference reflects an adaptation of the brain's face processing
machinery to this specific stimulus class (i.e., faces). The purpose of the
present study is to examine this property in greater detail and to specifically
elucidate the implication of internal face features (i.e., eyes, mouth, and
nose). To this end, I parameterized Gabor filters to match the spatial receptive
field of contrast sensitive neurons in the primary visual cortex (simple and
complex cells). Filter responses to a large number of face images were computed,
aligned for internal face features, and response-equalized
(“whitened”). The results demonstrate that the frequency
preference is caused by internal face features. Thus, the psychophysically
observed human frequency bias for face processing seems to be specifically
caused by the intrinsic spatial frequency content of internal face features