With the general availability of 3D digitizers and scanners, 3D graphical models have been used widely in a variety of applications. This has led to the development of search engines for 3D models. Especially, 3D head model classification and retrieval have received more and more attention in view of their many potential applications in criminal identifications, computer animation, movie industry and medical industry. This paper addresses the 3D head model classification problem using 2D subspace analysis methods such as 2D principal component analysis (2D PCA) and 2D fisher discriminant analysis (2DLDA). It takes advantage of the fact that the histogram is a 2D image, and we can extract the most useful information from these 2D images to get a good result accordingingly. As a result, there are two main advantages: First, we can perform less calculation to obtain the same rate of classification; second, we can reduce the dimensionality more than PCA to obtain a higher efficiency.