  Face recognition can benefit from the utilization of depth data captured
using low-cost cameras, in particular for presentation attack detection
purposes. Depth video output from these capture devices can however contain
defects such as holes or general depth inaccuracies. This work proposes a deep
learning face depth enhancement method in this context of facial biometrics,
which adds a security aspect to the topic. U-Net-like architectures are
utilized, and the networks are compared against hand-crafted enhancer types, as
well as a similar depth enhancer network from related work trained for an
adjacent application scenario. All tested enhancer types exclusively use depth
data as input, which differs from methods that enhance depth based on
additional input data such as visible light color images. Synthetic face depth
ground truth images and degraded forms thereof are created with help of PRNet,
to train multiple deep learning enhancer models with different network sizes
and training configurations. Evaluations are carried out on the synthetic data,
on Kinect v1 images from the KinectFaceDB, and on in-house RealSense D435
images. These evaluations include an assessment of the falsification for
occluded face depth input, which is relevant to biometric security. The
proposed deep learning enhancers yield noticeably better results than the
tested preexisting enhancers, without overly falsifying depth data when
non-face input is provided, and are shown to reduce the error of a simple
landmark-based PAD method.
