Automatic gender classification has many security and commercial applications. Various modalities have been
investigated for gender classification with face-based classification being the most popular. In some real-world scenarios the
face may be partially occluded. In these circumstances a classification based on individual parts of the face known as local
features must be adopted. The authors investigate gender classification using lip movements. They show for the first time that
important gender-specific information can be obtained from the way in which a person moves their lips during speech.
Furthermore, this study indicates that the lip dynamics during speech provide greater gender discriminative information than
simply lip appearance. They also show that the lip dynamics and appearance contain complementary gender information such
that a model which captures both traits gives the highest overall classification result. They use discrete cosine transform-based
features and Gaussian mixture modelling to model lip appearance and dynamics and employ the XM2VTS database for their
experiments. These experiments show that a model which captures lip dynamics along with appearance can improve gender
classification rates by between 16 and 21% compared with models of only lip appearance