Machine learning applied to perception: Decision images for gender classification
F Wichmann,
A Graf,
E P Simoncelli,
H Bülthoff and
B Schölkopf
Presented at:
Neural Information Processing Systems (NIPS*04), Vancouver BC, Dec 2004.
To be published in:
Advances in Neural Information Processing Systems 17
eds. L. K. Saul, Y. Weiss, and Leon Bottou,
pp. 1489--1496, May 2005.
© MIT Press, Cambridge, MA.
We study gender discrimination of human faces using a combination of
psychophysical classification and discrimination experiments together
with methods from machine learning. We reduce the dimensionality of a
set of face images using principal component analysis, and then train
a set of linear classifiers on this reduced representation (linear
support vector machines (SVMs), relevance vector machines (RVMs),
Fisher linear discriminant (FLD), and prototype (prot) classifiers)
using human classification data. Because we combine a linear
preprocessor with linear classifiers, the entire system acts as a
linear classifier, allowing us to visualise the decision-image
corresponding to the normal vector of the separating hyperplanes (SH)
of each classifier. We predict that the female-to-maleness transition
along the normal vector for classifiers closely mimicking human
classification (SVM and RVM) should be faster than the
transition along any other direction. A psychophysical discrimination
experiment using the decision images as stimuli is consistent with
this prediction.
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