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Charles A. Collin, Cheron Martin; Middle Spatial Frequencies are Needed for Face Recognition Only When Learned Faces are Unfiltered: Evidence from Spatial Frequency Thresholds for Matching. Journal of Vision 2004;4(8):431. doi: 10.1167/4.8.431.
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© ARVO (1962-2015); The Authors (2016-present)
A number of studies (Gold, Bennett, & Sekuler, 1999; Nasanen, 1999; see Parker & Costen, 2001 for review) have suggested that middle spatial frequencies (SFs) are optimal for face recognition. A few recent studies (Liu et al., 2000; Collin et al., 2003; Kornowski & Petersik, 2003) have cast doubt on this, suggesting that perhaps it is the overlap in SFs between learned and tested faces that is the more important factor in determining how well spatially filtered faces are recognized. The latter studies predict that if learned faces are filtered in the same way as the tested faces, little or no advantage of middle SFs for face recognition will be found. In the present set of experiments, we set out to test this prediction by obtaining SF thresholds for recognition of low-passed and high-passed faces in a match-to-sample task. This was done under conditions where the choice faces were either unfiltered, or filtered in the same way as the target face. On each trial, observers were presented with a single low-passed or high-passed target face in the center of the screen, which was to be matched to one of four choice faces at the bottom of the screen. Observers adjusted the SF cutoff of the target face using keyboard buttons, until they reached the point at which recognition was just possible (i.e., the SF threshold for face matching). In one condition, the choice faces remained unfiltered throughout. In another condition, they were filtered in real time at the same spatial-frequency cutoff as the target face. Ten subjects were tested in each condition. Our results show that subjects require middle SF information when attempting to match a filtered face to unfiltered faces, but not when attempting to match face images filtered in the same way. This suggests that the high efficacy of middle SFs in face recognition is task-dependent and may arise due to interference from non-middle SFs in unfiltered learned images.
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