  Quality assessment of fingerprints captured using digital cameras and
smartphones, also called fingerphotos, is a challenging problem in biometric
recognition systems. As contactless biometric modalities are gaining more
attention, their reliability should also be improved. Many factors, such as
illumination, image contrast, camera angle, etc., in fingerphoto acquisition
introduce various types of distortion that may render the samples useless.
Current quality estimation methods developed for fingerprints collected using
contact-based sensors are inadequate for fingerphotos. We propose Utility
guided Fingerphoto Quality Assessment (UFQA), a self-supervised dual encoder
framework to learn meaningful feature representations to assess fingerphoto
quality. A quality prediction model is trained to assess fingerphoto quality
with additional supervision of quality maps. The quality metric is a predictor
of the utility of fingerphotos in matching scenarios. Therefore, we use a
holistic approach by including fingerphoto utility and local quality when
labeling the training data. Experimental results verify that our approach
performs better than the widely used fingerprint quality metric NFIQ2.2 and
state-of-the-art image quality assessment algorithms on multiple publicly
available fingerphoto datasets.
