We attempt to understand visual classification in humans using both psy- chophysical and machine learning techniques. Frontal views of human faces were used for a gender classification task. Human subjects classi- fied the faces 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 perfor- mance of the learning algorithms was estimated using the face database with the true gender of the faces as labels, and also with the gender es- timated by the subjects.

This paper describes a new model for human visual classification that enables the recovery of image features that explain human subjects' performance on different visual classification tasks. Unlike previous methods, this algorithm does not model their performance with a single linear classifier operating on raw image pixels. This approach extracts more information about human visual classification than has been previously possible with other methods and provides a foundation for further exploration.

This paper describes a new model for human visual classification that enables the recovery of image features that explain human subjects' performance on different visual classification tasks. Unlike previous methods, this algorithm does not model their performance with a single linear classifier operating on raw image pixels. This approach extracts more information about human visual classification than has been previously possible with other methods and provides a foundation for further exploration. Papers published at the Neural Information Processing Systems Conference.