Felix A. Wichmann, Arnulf Graf, Heinrich Bülthoff, Eero Simoncelli, Bernhard Schölkopf
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 vec- tor machines (SVMs), relevance vector machines (RVMs), Fisher linear discriminant (FLD), and prototype (prot) classiﬁers) using human clas- siﬁcation data. Because we combine a linear preprocessor with linear classiﬁers, the entire system acts as a linear classiﬁer, allowing us to visu- alise the decision-image corresponding to the normal vector of the separ- ating hyperplanes (SH) of each classiﬁer. We predict that the female-to- maleness transition along the normal vector for classiﬁers closely mim- icking human classiﬁcation (SVM and RVM ) should be faster than the transition along any other direction. A psychophysical discrimina- tion experiment using the decision images as stimuli is consistent with this prediction.