Expression invariant 3D face recognition with a Morphable Model

TLDR

The study proposes an expression‑invariant 3D face‑recognition method that fits an identity/expression‑separated Morphable Model to shape data. The method fits the Morphable Model to shape data using a robust nonrigid ICP algorithm and is evaluated on noisy and neutral datasets, demonstrating maintained performance across conditions. The approach yields high recognition and retrieval rates, matching top vendor algorithms, even in noisy, uncooperative settings, and maintains performance on neutral data while relying solely on shape.

Abstract

We describe an expression-invariant method for face recognition by fitting an identity/expression separated 3D Morphable Model to shape data. The expression model greatly improves recognition and retrieval rates in the uncooperative setting, while achieving recognition rates on par with the best recognition algorithms in the face recognition great vendor test. The fitting is performed with a robust nonrigid ICP algorithm. It is able to perform face recognition in a fully automated scenario and on noisy data. The system was evaluated on two datasets, one with a high noise level and strong expressions, and the standard UND range scan database, showing that while expression invariance increases recognition and retrieval performance for the expression dataset, it does not decrease performance on the neutral dataset. The high recognition rates are achieved even with a purely shape based method, without taking image data into account.