Relevance vector machine for content-based retrieval of 3D head models

In this paper, we propose a novel 3D head model retrieval approach in which the queries are 2D face views instead of less readily available 3D head models. The basic idea is to characterize the corresponding relations between 2D view feature and 3D model feature based on a machine learning approach. Thus the subsequent feature matching can be carried out in 3D feature space. As an effective solution to regression problems, relevance vector machine is used in this paper to establish an association between 2D and 3D features. Experimental results show that our proposed 2D query based method is comparable with the direct 3D query based one.