Invariant Gender Identification

Abstract

Tolba, A. S., Invariant Gender Identification, Digital Signal Processing 11 (2001) 222–240 In this paper, we address the problem of gender identification using different neural network classifiers: a learning vector quantization (LVQ) network and a radial basis function (RBF) network. Our results indicate that it is more favorable to use either the LVQ network or the RBF network than any feature-based methods. We present results showing identification of gender with a hit rate of 100% in the case of a LVQ network and 98.04% in the case of an RBF network. When hair information was excluded, the best LVQ classifier resulted in 95.1% correct identification. We show that while the two models are nearly accurate, the RBF model learns the task considerably faster than the LVQ model. These results are favorable compared with eigen-decomposition-based techniques. The effect of head covers (e.g., a scarf) used by both men and women on system performance is studied.