  Facial landmark detection is a very fundamental and significant vision task
with many important applications. In practice, facial landmark detection can be
affected by a lot of natural degradations. One of the most common and important
degradations is the shadow caused by light source blocking. While many advanced
shadow removal methods have been proposed to recover the image quality in
recent years, their effects to facial landmark detection are not well studied.
For example, it remains unclear whether shadow removal could enhance the
robustness of facial landmark detection to diverse shadow patterns or not. In
this work, for the first attempt, we construct a novel benchmark to link two
independent but related tasks (i.e., shadow removal and facial landmark
detection). In particular, the proposed benchmark covers diverse face shadows
with different intensities, sizes, shapes, and locations. Moreover, to mine
hard shadow patterns against facial landmark detection, we propose a novel
method (i.e., adversarial shadow attack), which allows us to construct a
challenging subset of the benchmark for a comprehensive analysis. With the
constructed benchmark, we conduct extensive analysis on three state-of-the-art
shadow removal methods and three landmark detectors. The observation of this
work motivates us to design a novel detection-aware shadow removal framework,
which empowers shadow removal to achieve higher restoration quality and enhance
the shadow robustness of deployed facial landmark detectors.
