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An interesting problem in analysis of video data concerns design of algorithms that detect perceptually significant features in an unsupervised manner, for instance methods of machine learning for automatic classification of human expression. A geometric formulation of this genre of problems could be modeled with help of perceptual psychology. In this article, we outline one approach for a special case where video segments are to be classified according to expression of emotion or other similar facial motions. The encoding of realistic facial motions that convey expression of emotions for a particular person P forms a parameter space X_P whose study reveals the “objective geometry” for the problem of unsupervised feature detection from video. The geometric features and discrete representation of the space X_P are independent of subjective evaluations by observers. While the “subjective geometry” of X_P varies …