How re-training process affect the performance of no-reference image quality metric for face images

Xinwei Liu (1) , Christophe Charrier (1) , Pedersen Marius (2) , Patrick Bours (2)

1 Equipe SAFE - Laboratoire GREYC - UMR6072
2 NTNU - Norwegian University of Science and Technology [Gjøvik]

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

Mots clés: biometric quality assessment; face recognition; image quality metric; re-training; performance evaluation

Domaines: Cryptographie et sécurité [cs.CR]; Traitement des images [eess.IV]; Traitement du signal et de l'image [eess.SP]

Xinwei Liu, Christophe Charrier, Pedersen Marius, Patrick Bours. How re-training process affect the performance of no-reference image quality metric for face images. Media Watermarking, Security, and Forensics 2019 at Electronic Imaging IS&T, Jan 2019, Burligname, United States. ⟨hal-02115332⟩