  Landmark detection algorithms trained on high resolution images perform
poorly on datasets containing low resolution images. This deters the
performance of algorithms relying on quality landmarks, for example, face
recognition. To the best of our knowledge, there does not exist any dataset
consisting of low resolution face images along with their annotated landmarks,
making supervised training infeasible. In this paper, we present a
semi-supervised approach to predict landmarks on low resolution images by
learning them from labeled high resolution images. The objective of this work
is to show that predicting landmarks directly on low resolution images is more
effective than the current practice of aligning images after rescaling or
superresolution. In a two-step process, the proposed approach first learns to
generate low resolution images by modeling the distribution of target low
resolution images. In the second stage, the roles of generated images and real
low resolution images are switched and the model learns to predict landmarks
for real low resolution images from generated low resolution images. With
extensive experimentation, we study the impact of each of the design choices
and also show that prediction of landmarks directly on low resolution images
improves the performance of important tasks such as face recognition in low
resolution images.
