Accéder directement au contenu Accéder directement à la navigation
Communication dans un congrès

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

Abstract : 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.
Type de document :
Communication dans un congrès
Liste complète des métadonnées

https://hal.univ-angers.fr/hal-02528744
Contributeur : Okina Université d'Angers <>
Soumis le : mercredi 1 avril 2020 - 23:41:19
Dernière modification le : jeudi 2 avril 2020 - 10:13:42

Identifiants

Collections

Citation

Tõnis Uiboupin, Pejman Rasti, Hasan Demirel. Facial image super resolution using sparse representation for improving face recognition in surveillance monitoring. 2016 24th Signal Processing and Communication Application Conference (SIU), 2016, Zonguldak, Turkey. pp.437-440, ⟨10.1109/SIU.2016.7495771⟩. ⟨hal-02528744⟩

Partager

Métriques

Consultations de la notice

13