Landmark Localisation in 3D Face Data

Abstract

A comparison of several approaches that use graph matching and cascade filtering for landmark localization in 3D face data is presented. For the first method, we apply the structural graph matching algorithm “relaxation by elimination” using a simple “distance to local plane” node property and a “Euclidean distance” arc property. After the graph matching process has eliminated unlikely candidates, the most likely triplet is selected, by exhaustive search, as the minimum Mahalanobis distance over a six dimensional space, corresponding to three node variables and three arc variables. A second method uses state-of-the-art pose-invariant feature descriptors embedded into a cascade filter to localize the nose tip. After that, local graph matching is applied to localize the inner eye corners. We evaluate our systems by computing root mean square errors of estimated landmark locations against ground truth landmark localizations within the 3D Face Recognition Grand Challenge database. Our best system, which uses a novel pose-invariant shape descriptor, scores 99.77% successful localization of the nose and 96.82% successful localization of the eyes.