  Many mobile manufacturers recently have adopted Dual-Pixel (DP) sensors in
their flagship models for faster auto-focus and aesthetic image captures.
Despite their advantages, research on their usage for 3D facial understanding
has been limited due to the lack of datasets and algorithmic designs that
exploit parallax in DP images. This is because the baseline of sub-aperture
images is extremely narrow and parallax exists in the defocus blur region. In
this paper, we introduce a DP-oriented Depth/Normal network that reconstructs
the 3D facial geometry. For this purpose, we collect a DP facial data with more
than 135K images for 101 persons captured with our multi-camera structured
light systems. It contains the corresponding ground-truth 3D models including
depth map and surface normal in metric scale. Our dataset allows the proposed
matching network to be generalized for 3D facial depth/normal estimation. The
proposed network consists of two novel modules: Adaptive Sampling Module and
Adaptive Normal Module, which are specialized in handling the defocus blur in
DP images. Finally, the proposed method achieves state-of-the-art performances
over recent DP-based depth/normal estimation methods. We also demonstrate the
applicability of the estimated depth/normal to face spoofing and relighting.
