Spatial Content and Spatial Quantisation Effects in Face Recognition

Abstract

It has recently become apparent that if face images are degraded by spatial quantisation, or block averaging, there is a nonlinear acceleration of the decline in accuracy of recognition as block size increases. This suggests recognition requires a critical minimum range of object spatial frequencies. Two experiments were performed to clarify the phenomenon. In experiment 1, the speed and accuracy of recognition for six frontoparallel photographs of faces were measured. After familiarisation training sessions, the images were shown for 100 ms with 11, 21, and 42 pixels per face, horizontally measured. Transformations calculated to remove the same range of spatial frequencies were performed by means of quantisation, a Fourier low-pass filter, and Gaussian blurring. Although accuracy declined and speed increased in a significant, nonlinear manner in all cases as the image quality was reduced, it did so at a faster rate for the quantised images. In experiment 2, faces rated as being typical were shown at 9, 12, 23, and 45 pixels per face and with appropriate Fourier low-pass versions. The nonlinear decline was confirmed and it was shown that it could not be attributed to a ceiling effect. A further condition allowed quantised and Fourier low-pass conditions to be compared with an unstructured-noise condition of equal strength to that of the quantised images. These gave comparable, but slightly less impaired, recognition than the quantised images. It can be inferred from these results that the removal of a critical range of at least 8-16 cycles per face of information explains the step decline in recognition seen with quantised images. However, the decline found with quantised images is reinforced by internal masking from pixelisation.

Face perception: An integrative review of the role of spatial frequencies

“…Hence, the information contained in a small medium range of SFs contributes more to the face-recognition process than the information contained in all the other SFs (Costen, Parker & Craw, 1994Näsänen, 1999;Parker & Costen, 1999). However, though all these results indicate that privileged information can be found in medium-range SFs, the role of the SFs outside that range cannot be overlooked.…”

“…The Masking Approach Initially, the main objective of research focusing on image filtering was the search for the range of SFs that are critical for face recognition. Although many results seem to indicate that middle-range SFs are the critical ones (Costen, Parker & Craw, 1994, it has been demonstrated that a large group of SFs, some of which are very far from the middle range, are needed to resolve recognition tasks with a good level of efficiency. Likewise, other results Face Perception 9 suggest that HSFs can also play an important role in face recognition (Fiorentini, Maffei & Sandini, 1983).…”

“…Costen, Parker and Craw (1994) instructed subjects to learn six faces (which were equivalent in size, position and physical features) that were displayed on a screen for one second. During the test phase the subjects were instructed to recognize faces that were shown on the screen for only 100 ms. Each face in the test phase had 11, 21, and 42 pixels (5.5, 10.5 and 21 cycles/fw, respectively), had been low-pass filtered and had been blurred using Gaussian filters.…”

Diagnostic Colors Mediate Scene Recognition

“…In higher-level vision, numerous studies have examined how luminance cues supported the recognition of faces (Breitmeyer, 1984;Costen, Parker, & Craw, 1994;Fiorentini, Maffei, & Sandini, 1983;Sergent, 1986), objects (Parker et al, 1996), and scenes Parker, Lishman, & Hughes, 1992;Schyns & Oliva, 1994). It was found that fine-scale boundary edges (from high spatial frequencies) and coarser scale blobs (from low spatial frequencies) could selectively mediate different categorizations of the same stimuli (e.g., Schyns & Oliva, 1999).…”

Face Recognition Is Affected by Similarity in Spatial Frequency Range to a Greater Degree Than Within-Category Object Recognition.

“…Bachmann (1991), using a delayed match-to-sample task, found a rapid decrease in accuracy as coarse-quantized face images were reduced in resolution from 18 pixels per face width to 15. In related experiments, Costen et al (1994Costen et al ( , 1996, using a similar method to Bachmann's, found abrupt drop-offs in performance as faces were low-pass filtered at cutoffs less than 8 cycles per face or high-pass filtered at greater than 16 cycles per face. In experiments comparing human delayed match-to-sample performance with that of an ideal observer algorithm, Nasanen (1999) found a reduction in the efficiency function of participants when frequencies around 8 -13 cycles per face were masked by noise.…”