  Very low resolution (VLR) image recognition corresponds to classifying images
with resolution 16x16 or less. Though it has widespread applicability when
objects are captured at a very large stand-off distance (e.g. surveillance
scenario) or from wide angle mobile cameras, it has received limited attention.
This research presents a novel Dual Directed Capsule Network model, termed as
DirectCapsNet, for addressing VLR digit and face recognition. The proposed
architecture utilizes a combination of capsule and convolutional layers for
learning an effective VLR recognition model. The architecture also incorporates
two novel loss functions: (i) the proposed HR-anchor loss and (ii) the proposed
targeted reconstruction loss, in order to overcome the challenges of limited
information content in VLR images. The proposed losses use high resolution
images as auxiliary data during training to "direct" discriminative feature
learning. Multiple experiments for VLR digit classification and VLR face
recognition are performed along with comparisons with state-of-the-art
algorithms. The proposed DirectCapsNet consistently showcases state-of-the-art
results; for example, on the UCCS face database, it shows over 95\% face
recognition accuracy when 16x16 images are matched with 80x80 images.
