This paper reports a statistical study of human face shape whose overall goal is to identify and characterize salient components of facial structure for human perception and communicative behavior. A large database of 3D faces has been constructed and is being analyzed for differences in ethnicity, sex, and posture. For each of more than 300 faces varying in race/ethnicity (Japanese vs. Caucasian) and sex, nine postures (smiling, producing vowels, etc.) were recorded. Principal Components Analysis (PCA) and Linear Discriminant Analysis (LDA) were used to reduce the dimensionality of the data and to provide simple, yet reliable reconstruction of any face from components that correspond to the sex, ethnicity, and posture of the face. Thus, it appears that any face can be reconstructed from a small set of linear and intuitively salient components. Psychophysical tests confirmed that the shape is sufficient to estimate sex and ethnicity. Subjects were asked to judge the sex and ethnicity of (a) natural faces and (b) faces synthesized by randomly combining Principal Component coefficients within the database. Subjects successfully discriminated ethnicity and sex independently of posture, verifying that different combinations of components are required and in differing amounts. Finally, implications of these results on animation and face recognition are discussed, incorporating results of studies currently underway that examine the”face print” residue of the sex-ethnicity factor analysis.