3D face shape prediction from a frontal image using cylindrical coordinates and partial least squares

摘要

This paper addresses the problem of linearly approximating 3D shape from intensities in the context of facial analysis. In other words, given a frontal pose grayscale input face, the direct estimation of its 3D structure is sought through a regression matrix. Approaches falling into this category generally assume that both 2D and 3D features are defined under Cartesian schemes, which is not optimal for the task of novel view synthesis. The current article aims to overcome this issue by exploiting the 3D structure of faces through cylindrical coordinates, aided by the partial least squares regression. In the context of facial shape analysis, partial least squares builds a set of basis faces, for both grayscale and 3D shape spaces, seeking for maximizing shared covariance between projections of the data along the basis faces. Experimental tests show how the cylindrical representations are suitable for the purposes of linear regression, resulting in a benefit for the generation of novel facial views, showing a potential use in model based face identification.