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Modèles statistiques avancés pour la segmentation non supervisée des images dégradées de l'iris

Abstract : Iris is considered as one of the most robust and efficient modalities in biometrics because of its low error rates. These performances were observed in controlled situations, which impose constraints during the acquisition in order to have good quality images. The renouncement of these constraints, at least partially, implies degradations in the quality of the acquired images and it is therefore a degradation of these systems’ performances. One of the main proposed solutions in the literature to take into account these limits is to propose a robust approach for iris segmentation. The main objective of this thesis is to propose original methods for the segmentation of degraded images of the iris. Markov chains have been well solicited to solve image segmentation problems. In this context, a feasibility study of unsupervised segmentation into regions of degraded iris images by Markov chains was performed. Different image transformations and different segmentation methods for parameters initialization have been studied and compared. Optimal modeling has been inserted in iris recognition system (with grayscale images) to produce a comparison with the existing methods. Finally, an extension of the modeling based on the hidden Markov chains has been developed in order to realize an unsupervised segmentation of the iris images acquired in visible light
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Meriem Yahiaoui. Modèles statistiques avancés pour la segmentation non supervisée des images dégradées de l'iris. Traitement du signal et de l'image [eess.SP]. Université Paris-Saclay, 2017. Français. ⟨NNT : 2017SACLL006⟩. ⟨tel-01591556⟩

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