We aimed to examine the cognitive question of why human observers have difficultly in distinguishing two extremely similar faces of people of different genders, by using hybrid images (HIs). For this purpose, we proposed a computational model of the cognitive processing in the brain of multi-scale perception, which incorporates two different roles of high- and low-spatial scales in face recognition. This model was based on the multi-scale correspondence between the sizes of the filters and images in the Gabor pyramid. Multi-scale correspondence with relatively small Gabor kernels demonstrated that the scale similarity curves were qualitatively consistent with the gain functions for spatial frequency in the Gaussian filters. Locally high-scale similarities for both the low- and the high-pass filtered images indicated that the face in the HI was misrecognized as the face in the counterpart filtered image. Smaller-scale similarity differences with greater spatial frequency overlap of the low- and high-pass filters of the HI demonstrated that human observers fail to employ appropriate combinations of high- and low-scale representations to distinguish extremely similar faces. The larger Gabor kernels suggested that human observers fail to identify the face in the HI at the low resolution.
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Computer Science Applications
- Cognitive Neuroscience