" 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 …

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 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 …

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 …

Quality scores provide a measure to evaluate the utility of biometric samples for biometric
recognition. Biometric recognition systems require high-quality samples to achieve optimal …

In this paper, we propose a multi-task convolutional neural network which produces an
image quality vector for an input face image. This image quality vector contains the face …

In this paper, we proposed a novel biometric quality assessment (BQA) method for face
images and explored its applications in face recognition. Here, we considered five …

The varying quality of face images is an important challenge that limits the effectiveness of
face recognition technology when applied in real-world applications. Existing face image …

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 …