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With systems for acquiring 3D surface data being evermore commonplace, it hasbecome important to reliably extract specific shapes from the acquired data. Inthe presence of noise and occlusions, this can be done through the use ofstatistical shape models, which are learned from databases of clean examples ofthe shape in question. In this paper, we review, analyze and compare differentstatistical models: from those that analyze the variation in geometry globallyto those that analyze the variation in geometry locally. We first review howdifferent types of models have been used in the literature, then proceed todefine the models and analyze them theoretically, in terms of both theirstatistical and computational aspects. We then perform extensive experimentalcomparison on the task of model fitting, and give intuition about which type ofmodel is better for a few applications. Due to the wide availability ofdatabases of high-quality data, we use the human face as the specific shape wewish to extract from corrupted data.

Review of Statistical Shape Spaces for 3D Data with Comparative Analysis for Human Faces