In this paper we develop a Quality Assessment approach for face recognition based on deep
learning. The method consists of a Convolutional Neural Network, FaceQnet, that is used to …

Face quality assessment aims at estimating the utility of a face image for the purpose of
recognition. It is a key factor to achieve high face recognition performances. Currently, the …

" The output of a computerised system can only be as accurate as the information entered
into it." This rather trivial statement is the basis behind one of the driving concepts in …

Abstract In recent years, Face Image Quality Assessment (FIQA) has become an
indispensable part of the face recognition system to guarantee the stability and reliability of …

Convolutional neural network (CNN) based approaches are the state of the art in various
computer vision tasks including face recognition. Considerable research effort is currently …

The performance of face recognition system degrades when the variability of the acquired
faces increases. Prior work alleviates this issue by either monitoring the face quality in pre …

The performance of face analysis and recognition systems depends on the quality of the
acquired face data, which is influenced by numerous factors. Automatically assessing the …

Face image quality can be defined as a measure of the utility of a face image to automatic
face recognition. In this paper, we propose (and compare) two methods for learning face …

Face image quality is an important factor to enable high-performance face recognition
systems. Face quality assessment aims at estimating the suitability of a face image for the …

Face image quality assessment (FIQA) estimates the utility of the captured image in
achieving reliable and accurate recognition performance. This work proposes a novel FIQA …