Landmark Localisation in 3D Face Data

A comparison of several approaches that use graph matching and cascade filtering for landmark localisation 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 localise the nose tip. After that, local graph matching is applied to localise the inner eye corners. We evaluate our systems by computing root mean square errors of estimated landmark locations against ground truth landmark localisations within the 3D Face Recognition Gran...

• Type: Journal
• Year: 2009
• Where: AVSS
• Authors: Marcelo Romero, Nick Pears
• Keywords: AVSS 2009, Facial Landmark Localisation, Landmark Localisation, Signal Processing, Successful Localisation