Although humans are extremely good at classifying gender from facial images, experiments have shown that most people have difficulty in classify gender from images were the faces are hairless. Furthermore, error rates in gender classification using low resolution facial images increases almost ten fold.
It would represent a major commercial advantage if computers could have reliable vision capabilities for classifying a person's gender, particularly from low resolution images. Although gender classification has been investigated from both a psychological and computational perspective, relatively few learning based vision methods are known for gender classification.
Golomb et al. in “SEXNET: A neural network identifies sex from human faces,” Advances in Neural Information Processing Systems, pp. 572–577, 1991, described a fully connected two-layer neural network to identify gender from human face images consisting of 30×30 pixel images.
Cottrell et al. in “Empath: Face, emotion, and gender recognition using holons,” Advances in Neural Information Processing Systems, pp. 564–571, 1991 also applied neural networks for face emotion and gender recognition. They reduced the dimensionality of a set of 4096×4096 images to 40×40 via an auto-encoder network. Those vectors were then given as inputs to another one layer network for training and recognition.
Brunelli et al, in “HyperBF networks for gender classification,” Proceedings of the DARPA Image Under-standing Workshop, pp. 311–314, 1992 developed HyperBF networks for gender classification in which two competing RBF networks, one for male and the other one for female, were trained using sixteen geometric features, e.g., pupil to eyebrow separation, eyebrow thickness, and nose width, as inputs.
Instead of using a raster scan vector of gray levels to represent face images, Wiskott et al. in “Face recognition and gender determination,” Proceedings of the International Workshop on Automatic Face and Gesture Recognition, pp. 92–97, 1995 described a system that used labeled graphs of two-dimensional views to describe faces. The nodes were labeled with jets which are a special class of local templates computed on the basis of wavelet transform, and the edges were labeled with distance vectors. They used a small set of controlled model graphs of males and females to encode the general face knowledge.
More recently, Gutta et al. in “Gender and ethnic classification of Face Images,” Proceedings of the IEEE International Automatic Face and Gesture Recognition, pp. 194–199, 1998 proposed a hybrid method which consists of ensemble of neural networks (RBFs) and inductive decision trees.
Using computer vision to determine the gender of subject faces can be used in various application areas, such as the gathering of population gender-statistics from patrons at entertainment/amusement/sports parks. Gender classification can also be used by television network viewer-rating studies. Gender specific computer vision can further have application in such fields as automated security/surveillance systems, demographic studies, safety monitoring systems, and human interfaces to computers.