Human and Machine Recognition of Fourier-Bessel Filtered Face Images

Zana, Yossi; César, Roberto M.; Mena‐Chalco, Jesús Pascual

doi:10.1109/fgr.2006.66

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…The proposed method can also be useful for the understanding of human face processing and we are currently developing psychophysical experiments to establish the level of its relation to biological systems. Preliminary results [44] indicate a similar pattern of sensitivity of the human and FBT-based algorithm to polar frequency filtering.…”

Section: Discussionsupporting

confidence: 57%

Local approach for face verification in polar frequency domain

Zana¹,

César²,

Feris³

et al. 2006

Image and Vision Computing

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Section: Discussionsupporting

confidence: 57%

Local approach for face verification in polar frequency domain

Zana¹,

César²,

Feris³

et al. 2006

Image and Vision Computing

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“…The main motivation for this study was to improve the predictability value and increase the biologically inspired content of high-level visual tasks such as human object recognition models (7). The main contributions of this study were a) demonstrating for the first time that human visual face processing could involve the selective use of polar frequency components (8), and b) reporting direct empirical support for a recently proposed computational face recognition model (6).…”

Section: Introductionmentioning

confidence: 89%

A novel polar-based human face recognition computational model

Zana

Mena‐Chalco

César

2009

Braz J Med Biol Res

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Motivated by a recently proposed biologically inspired face recognition approach, we investigated the relation between human behavior and a computational model based on Fourier-Bessel (FB) spatial patterns. We measured human recognition performance of FB filtered face images using an 8-alternative forced-choice method. Test stimuli were generated by converting the images from the spatial to the FB domain, filtering the resulting coefficients with a band-pass filter, and finally taking the inverse FB transformation of the filtered coefficients. The performance of the computational models was tested using a simulation of the psychophysical experiment. In the FB model, face images were first filtered by simulated V1-type neurons and later analyzed globally for their content of FB components. In general, there was a higher human contrast sensitivity to radially than to angularly filtered images, but both functions peaked at the 11.3-16 frequency interval. The FB-based model presented similar behavior with regard to peak position and relative sensitivity, but had a wider frequency band width and a narrower response range. The response pattern of two alternative models, based on local FB analysis and on raw luminance, strongly diverged from the human behavior patterns. These results suggest that human performance can be constrained by the type of information conveyed by polar patterns, and consequently that humans might use FB-like spatial patterns in face processing.

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Human and Machine Recognition of Fourier-Bessel Filtered Face Images

Abstract: Motivated by a recently proposed biologically-inspired

Cited by 2 publications

References 22 publications

Local approach for face verification in polar frequency domain

Local approach for face verification in polar frequency domain

A novel polar-based human face recognition computational model

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