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.