  Many recent works have reconstructed distinctive 3D face shapes by
aggregating shape parameters of the same identity and separating those of
different people based on parametric models (e.g., 3D morphable models
(3DMMs)). However, despite the high accuracy in the face recognition task using
these shape parameters, the visual discrimination of face shapes reconstructed
from those parameters is unsatisfactory. The following research question has
not been answered in previous works: Do discriminative shape parameters
guarantee visual discrimination in represented 3D face shapes? This paper
analyzes the relationship between shape parameters and reconstructed shape
geometry and proposes a novel shape identity-aware regularization(SIR) loss for
shape parameters, aiming at increasing discriminability in both the shape
parameter and shape geometry domains. Moreover, to cope with the lack of
training data containing both landmark and identity annotations, we propose a
network structure and an associated training strategy to leverage mixed data
containing either identity or landmark labels. We compare our method with
existing methods in terms of the reconstruction error, visual
distinguishability, and face recognition accuracy of the shape parameters.
Experimental results show that our method outperforms the state-of-the-art
methods.
