  Selfie-based biometrics has great potential for a wide range of applications
from marketing to higher security environments like online banking. This is now
especially relevant since e.g. periocular verification is contactless, and
thereby safe to use in pandemics such as COVID-19. However, selfie-based
biometrics faces some challenges since there is limited control over the data
acquisition conditions. Therefore, super-resolution has to be used to increase
the quality of the captured images. Most of the state of the art
super-resolution methods use deep networks with large filters, thereby needing
to train and store a correspondingly large number of parameters, and making
their use difficult for mobile devices commonly used for selfie-based.
  In order to achieve an efficient super-resolution method, we propose an
Efficient Single Image Super-Resolution (ESISR) algorithm, which takes into
account a trade-off between the efficiency of the deep neural network and the
size of its filters. To that end, the method implements a novel loss function
based on the Sharpness metric. This metric turns out to be more suitable for
increasing the quality of the eye images. Our method drastically reduces the
number of parameters when compared with Deep CNNs with Skip Connection and
Network (DCSCN): from 2,170,142 to 28,654 parameters when the image size is
increased by a factor of x3. Furthermore, the proposed method keeps the sharp
quality of the images, which is highly relevant for biometric recognition
purposes. The results on remote verification systems with raw images reached an
Equal Error Rate (EER) of 8.7% for FaceNet and 10.05% for VGGFace. Where
embedding vectors were used from periocular images the best results reached an
EER of 8.9% (x3) for FaceNet and 9.90% (x4) for VGGFace.
