  Surveillance scenarios are prone to several problems since they usually
involve low-resolution footage, and there is no control of how far the subjects
may be from the camera in the first place. This situation is suitable for the
application of upsampling (super-resolution) algorithms since they may be able
to recover the discriminant properties of the subjects involved. While general
super-resolution approaches were proposed to enhance image quality for
human-level perception, biometrics super-resolution methods seek the best
"computer perception" version of the image since their focus is on improving
automatic recognition performance. Convolutional neural networks and deep
learning algorithms, in general, have been applied to computer vision tasks and
are now state-of-the-art for several sub-domains, including image
classification, restoration, and super-resolution. However, no work has
evaluated the effects that the latest proposed super-resolution methods may
have upon the accuracy and face verification performance in low-resolution
"in-the-wild" data. This project aimed at evaluating and adapting different
deep neural network architectures for the task of face super-resolution driven
by face recognition performance in real-world low-resolution images. The
experimental results in a real-world surveillance and attendance datasets
showed that general super-resolution architectures might enhance face
verification performance of deep neural networks trained on high-resolution
faces. Also, since neural networks are function approximators and can be
trained based on specific objective functions, the use of a customized loss
function optimized for feature extraction showed promising results for
recovering discriminant features in low-resolution face images.
