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improve the performance of 3D face matching. In the next paragraphs, we elaborate on these
observations.
The automatically selected anthropometric landmarks have a reasonable performance on
the UND face scans, but are not reliable enough for effective 3D face matching in the two
other sets. The contours perform well for retrieval and verification purposes in the UND
face set. However, their performance drops significantly for the other two sets, because
the contour curves cannot be effectively used in case of facial deformations. The use of
the model coefficients consistently outperforms both the landmark-based and contour-based
face matching. Besides the difference in performance, the three methods differ in running
time as well. The landmark-based method matches two faces using only 15 coordinates,
whereas the contour-based method matches two faces using 135 coordinates. The coefficient-
based method matches faces using 99 weights times the number of fitted components. So, the
coefficient-based method using four components has approximately the same running time as
the contour-based method.
The observation that multiple (four or seven) components increases the performance of our
face matching holds for all results except the landmark- and contour-based methods in the
GAVAB set. The problemwith this set is that a low quality scan of a person looking up or down
causes artifacts on and around the nose. In such cases a more accurate fit of the face model's
nose harms, because the performance of landmark- and contour-based methods are heavily
dependent on an accurate selection of the nose tip. Although the face matching improves from
the single to multiple component case, there is no consensus for the four or seven component
case. The use of either four or seven components causes either a marginal increase or decrease
of the evaluation scores. Although face matching with the use of 1000 model coefficients is
usually referred to as time efficient, one could argue for the use four components instead of
seven, because the number of coefficients is smaller.
Comparison.
Blanz et al. (2007) achieved a 96% RR for 150 queries in a set of 150 faces
(from the FRGC v.1). To determine the similarity of two face instances, they computed the
scalar product of the 1000 obtained model coefficients. In this chapter, we achieved 98% RR
on the UND set using the three selected contour curves, and 100% RR with the use of our
model coefficients.
4.6 Concluding Remarks
Where other methods need manual initialization, we presented a fully automatic 3D face
morphing method that produces a fast and accurate fit for the morphable face model to 3D
scan data. On the basis of a global-to-local fitting scheme the face model is coarsely fitted to
the automatically segmented 3D face scan. After the coarse fitting, the face model is either
finely fitted as a single component or as a set of individual components. Inconsistencies at the
borders are resolved using an easy to implement post processing method. Results show that
the use of multiple components produces a tighter fit of the face model to the face scan, but
assigned anthropometric landmarks may lose their reliability for 3D face identification.
We also presented a method for establishing accurate correspondences among 3D face
scans. With the use of an initial morphable face model and a set of predefined components,
we are able to produce accurate model fits to 3D face data with noise and holes. Afterwards,
