Facial image super resolution using sparse representation for improving face recognition in surveillance monitoring

TitreFacial image super resolution using sparse representation for improving face recognition in surveillance monitoring
Type de publicationCommunication
TypeCommunication avec actes dans un congrès
Année2016
LangueAnglais
Date du colloque16-19/05/2016
Titre du colloque2016 24th Signal Processing and Communication Application Conference (SIU)
Titre des actes ou de la revue2016 24th Signal Processing and Communication Application Conference (SIU)
Pagination437-440
AuteurUiboupin, Tõnis , Rasti, Pejman , Demirel, Hasan
PaysTurquie
EditeurIEEE
VilleZonguldak
ISBN978-1-5090-1679-2
Mots-clésface recognition, Hidden Markov Model, Super resolution, Support Vector Machine, surveillance videos
Résumé en anglais

Due to importance of security in the society, monitoring activities and recognizing specific people through surveillance video camera is playing an important role. One of the main issues in such activity rises from the fact that cameras do not meet the resolution requirement for many face recognition algorithm. In order to solve this issue, in this paper we are proposing a new system which super resolve the image using sparse representation with the specific dictionary involving many natural and facial images followed by Hidden Markov Model and Support vector machine based face recognition. The proposed system has been tested on many well-known face databases such as FERET, HeadPose, and Essex University databases as well as our recently introduced iCV Face Recognition database (iFRD). The experimental results shows that the recognition rate is increasing considerably after apply the super resolution by using facial and natural image dictionary.

URL de la noticehttp://okina.univ-angers.fr/publications/ua17507
DOI10.1109/SIU.2016.7495771
Lien vers le document en ligne

https://ieeexplore.ieee.org/document/7495771/