Traditional discriminate analysis treats all the involved classes equally in the computation of the between-class scatter matrix. However, we find that for many vision tasks, the classes to be processed are not equal in perception, i.e. a distance metric can be defined between the classes. Typical examples include head pose classification and age estimation. Aiming at this category of classification problem, this paper proposes a novel discriminant analysis method, called Class Distance based Discriminant Analysis (CDDA). In CDDA, the perceptional distance between two classes is exploited to weight the outer product in the between-class scatter computation, to concentrate more on the classes difficult to separate. Another novelty of CDDA is that to preserve the within-class local structure of multimodal labeled data, the within-class scatter is re-defined by complementing the similarity of the samples pairs in the nearby classes. The method is then applied to head pose classifi...