A Perceptron Reveals the Face of Sex
Recognizing the sex of conspecifics is important. Humans rely primarily on visual pattern recognition for this task. A wide variety of linear and nonlinear models have been developed to understand this task of sex recognition from human faces.' These models have used both pixelbased and feature-based representations of the face for input. Fleming and Cottrell (1990) and Golomb et a!. (1991) utilized first an autoencoder compression network on a pixel-based representation, and then a classification network. Brunelli and Poggio (1993) used a type of radial basis function network with geometrical face measurements as input. O'Toole and colleagues (1991, 1993) represented faces as principal components. When the hidden units of an autoencoder have a linear output function, then the N hidden units in the network span the first N principal components of the input (Baldi and Hornik 1989). Bruce et al. (1993) constructed a discriminant function for sex with 2-D and 3-D facial measures. In this note we compare the performance of a simple perceptron and a standard multilayer perceptron (MLP) on the sex classification task. We used a range of spatial resolutions of the face to determine how the reliability of sex discrimination is related to resolution. A normalized pixel-based representation was used for the faces because it explicitly retained texture and shape information while also maintaining geometric relationships. We found that the linear perceptron model can classify sex from facial images with 81% accuracy, compared to 92% accuracy with compression coding on the same data set (Golomb et al. 1991). The advantage of using a simple linear perceptron with normalized pixelbased inputs is that it allows us to see explicitly those regions of the face