Insights from Machine Learning Applied to Human Visual Classification

We attempt to understand visual classification in humans using both psychophysical andmachine learning techniques. Frontal views of human faces were used for a gender classification task. Human subjects classified thefaces and their gender judgment, reaction time and confidence rating were recorded. Several hyperplane learning algorithms were used on the same classification task using the Principal Components of the texture and shape representation of the faces. The classification performance ofthe learning algorithms was estimated using the face database with the true gender of the faces as labels, and also with the gender estimated bythe subjects.