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RESEARCH PRODUCT

Sex Classification of Face Areas

subject

description

Human subjects and an artificial neural network, composed of an autoassociative memory and a perceptron, gender classified the same 160 frontal face images (80 male and 80 female). All 160 face images were presented under three conditions (1) full face image with the hair cropped (2) top portion only of the Condition 1 image (3) bottom portion only of the Condition 1 image. Predictions from simulations using Condition 1 stimuli for training and testing novel stimuli in Conditions 1, 2, and 3, were compared to human subject performance. Although the network showed a fair ability to generalize learning to new stimuli under the three conditions, performing from 66 to 78% correctly on novel faces, and predicted main effects, a more detailed comparison with the human data was not as promising. As expected, human accuracy declined with decreased image area, but showed a surprising interaction between the sex of the face and the partial image conditions. The network failed to predict this interaction, or the likelihood of correct human classification for a particular face. This analysis on an item level raises concern about the psychological relevance of the model.