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Title: Spatial frequencies and face recognition
Author: Costen, Nicholas Paul
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 1994
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If face images are degraded by spatial quantisation there is a non-linear acceleration of the decline of recognition accuracy as block-size increases, suggesting recognition requires a critical minimum range of object spatial frequencies. These may define the facial configuration, reflecting the structural properties allowing differentiation of faces. Experiment 1 measured speed and accuracy of recognition of six fronto-parallel faces shown with 11, 21 and 42 pixels/face, produced by quantisation, a Fourier low-pass filter and Gaussian blurring. Performance declined with image quality in a significant, non-linear manner, but faster for the quantised images. Experiment 2 found some of this additional decline was due to frequency-domain masking. Experiment 3 compared recognition for quantised, Fourier low-pass and high-pass versions, recognition was only impaired when the frequency limit exceeded the range 4.5-12.5 cycles/face. Experiment 4 found this was not due to contrast differences. Experiments 5, 6 and 7 used octave band-pass filters centred on 4.14, 9.67 and 22.15 cycles/face, varying view-point for both sequential matching and recognition. The spatial frequency effect was not found for matching, but was for recognition. Experiment 8 also measured recognition of band-passed images, presented with octave bands centred on 2.46-50.15 cycles/face and at 0-90 degrees from fronto-parallel. Spatial frequency effects were found at all angles, with best performance for semi-profile images and 11.10 cycles/face. Experiment 9 replicated this, with perceptually equal contrasts and the outer facial contour removed. Modeling showed this reflected a single spatial-frequency channel two octaves wide, centred on 9 cycles/face. Experiment 10 measured response time for successive matching of faces across a size-disparity, finding an asymmetrical effect.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Pattern recognition & image processing Pattern recognition systems Pattern perception Image processing