  A novel methodology for gender classification is presented in this paper. It
extracts feature from local region of a face using gray color intensity
difference. The facial area is divided into sub-regions and GDP histogram
extracted from those regions are concatenated into a single vector to represent
the face. The classification accuracy obtained by using support vector machine
has outperformed all traditional feature descriptors for gender classification.
It is evaluated on the images collected from FERET database and obtained very
high accuracy.
