How re-training process affect the performance of no-reference image quality metric for face images
Résumé
The accuracy of face recognition systems is significantly affected by the quality of face sample images.
There are many existing no-reference image quality metrics (IQMs) that are able to assess natural image
quality by taking into account similar image-based quality attributes. Previous study showed that IQMs
can assess face sample quality according to the biometric system performance. In addition, re-training
an IQM can improve its performance for face biometric images. However, only one database was used in
the previous study, and it contains only image-based distortions. In this paper, we propose to extend the
previous study by use multiple face database including FERET color face database and apply multiple
setups for the re-training process in order to investigate how the re-training process affect the
performance of no-reference image quality metric for face biometric images. The experimental results
show that the performance of the appropriate IQM can be improved for multiple databases, and
different re-training setups can influence the IQM's performance