This paper claims that human various body shapes create individual eigenspaces; as a result, the classical appearance-based model cannot be effective for recognizing human postures. We introduce figure effect in the eigenspaces due to different human body shapes in this particular study. The study proposes an organized eigenspace tuning method for overcoming the preceding problem. Since the proposed method tunes the classical eigenspaces for human posture recognition, we define this phenomenon eigenspace tuning. Generation of a tuned eigenspace (TES) is an organized method where some of similar eigenspaces are selected according to MDD (minimum description deviation) method and a mean if them is taken. In fact, the TES is an optimized visual appearance of various human models that minimizes the fluctuation of MDL (mean description length) between training and testing feature spaces. We have tested the proposed approach on a number of human models considering their various body shapes, and significance of the method to the recognition rates has been demonstrated.