Machine learning applied to perception: decision-images for gender classification

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 [1]) 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.