Table 3 An overview of the 3D face landmarking algorithms
Work | Highlights of the method | Domain knowledge used | Landmark types |
|---|---|---|---|
Lu & Jain [88], 2006 | Shape index and cornerness information are fused into a field where extrema are searched at conjectured locations. Since face orientation is estimated, the method is robust against pose (i.e., yaw). | Nose tip is detected as the peak of the central vertical profile. Prior location probability of the eye and mouth corners vis-á-vis the nose tip. Anthropometric distances between landmark points measured in world coordinate system form a constraint set. | Mouth, inner eye corners, nose and chin tip.
|
Gökberk et al. [87], 2006 | An Average Face Model with 10 landmark points is aligned to the scene face via Iterative Closest Point algorithm. Initialized landmark positions are corrected via shape descriptors of Gaussian curvature, mean curvature, surface normals and landmark distances to the face symmetry plane. | 3D Average Face Model introduces both face geometry and local shape information. | 10 landmarks (m 7 plus philtruma, nasion and chin).
|
Conde & Cabello [94], 2006 | Mean curvature field of the face reveals the high curvature extrema; spin images at these extrema are classified via SVM as eye inner corners and nose tip. | None. | Endocanthionb and nose tip.
|
Akagunduz & Ulusoy [91], 2007 | Mean and Gaussian curvatures are calculated at many scales, and organized as a space-scale Gaussian pyramid (UVS). Surface shape properties within the connected components in the UVS space are investigated as being eye pits, chin and nose protuberances. | Topological graph to regularize the search is only suggested. | Eye pits, nose tip and pit, chin.
|
Salah & Akarun [95], 2006 and Dibeklioǧlu et al. [90], 2008 | Gabor jets are statistically modeled as incremental mixture of factor analyzers (IMoFA) to generate a lower-dimensional manifold. IMoFA is run on the difference image of the Gaussian and mean curvature fields. | Nose tip heuristics. | m 7 landmark set.
|
Nair & Cavallaro [93], 2009 | PDM: Point Distribution Model, i.e., a parametrized model of the 49 3D landmark configurations is computed. The PDM is fitted to the face driven by local curvedness and shape index information. | (i) PCA model of the 49 landmark points; (ii) face heuristics to prune out combinations of candidate landmarks to arrive to plausible shapes. | 49 upper face landmarks.
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