Abstract-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 Grand Challenge database. Our best system, which uses a novel pose-invariant shape descriptor, scores 99.77% successful localisation of the nose and 96.82% successful localisation of the eyes.
Romero and Pears REF compared structural graph matching and cascade filtering on landmark localization in 3D face data.
Landmark Localisation in 3D Face Data
2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Computer Science