Fitting a 3D morphable model to edges: A comparison between hard and soft correspondences

Authors: Bas, A., Smith, W.A.P., Bolkart, T. and Wuhrer, S.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 10117 LNCS

Pages: 377-391

eISSN: 1611-3349

ISSN: 0302-9743

DOI: 10.1007/978-3-319-54427-4_28

Abstract:

In this paper we explore the problem of fitting a 3D morphable model to single face images using only sparse geometric features (edges and landmark points). Previous approaches to this problem are based on nonlinear optimisation of an edge-derived cost that can be viewed as forming soft correspondences between model and image edges. We propose a novel approach, that explicitly computes hard correspondences. The resulting objective function is non-convex but we show that a good initialisation can be obtained efficiently using alternating linear least squares in a manner similar to the iterated closest point algorithm. We present experimental results on both synthetic and real images and show that our approach outperforms methods that use soft correspondence and other recent methods that rely solely on geometric features.

Source: Scopus