Learning 3D Face Models for shape based retrieval

In this paper, we evaluate the effect of learning algorithms, unsupervised and supervised, for 3D face model retrieval using a global shape feature. We used the dataset and protocol of SHREC 2007 3D Face Models Track (SHREC 2007 3DFMT) for the evaluation. Unlike the entrants for the track, we used global shape features to capture overall geometric shape of faces, e.g., that of foreheads. One of the global features, as it is, produced Mean Average Precision Highly Relevant (MAPH) figure of 0.84, outperforming the top finisher of the SHREC 2007 3DFMT whose MAPH=0.66. Learning was quite effective; for the same global feature, an unsupervised learning method produced MAPH=0.90, and a simple supervised learning method produced an “ideal” performance of MAPH=1.0..