Biometric recognition by multimodal face and iris using FFT and SVD methods With Adaptive Score Normalization

Mansoura, Lemmouchi; Athamena, Noureddine; Assas, Ouarda; Abdessemed, Y.

doi:10.1109/icocs.2019.8930748

Cited by 4 publications

(1 citation statement)

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“…This descriptor can be used to make a decision by finding a corresponding iris within a database of iris descriptors. For that, we compute the Euclidean distance, which achieves good results in biometric recognition systems, especially regarding iris and facial characteristics [38], [39]. Using the Euclidean distance is a general matching approach, typically employed in mathematical geometry, which yields a distance of zero for an identical match between the query iris and those in the database.…”

Section: 5-template Matchingmentioning

confidence: 99%

Algorithm for Iris recognition based on contourlet Transform and Entropy

Ezzaki

Idrissi

Moreno

et al. 2020

ELCVIA

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The iris is one of the most secure biometric types of information that is widely employed in authentication systems. In this paper we present a method for iris recognition based on the Contourlet Transform and Shannon Entropy which entails (i) the detection and segmentation of the iris, (ii) its normalization, (iii) the application of the Contourlet Transform, (iv) the generation of the iris descriptor, and (v) the matching between the query iris and those in the database. The proposed method has been tested with images taken from the popular CASIA-V4 and UBIRIS.v1 datasets and compared against other six recent iris recognition algorithms using the statistics EER, AUC and ARR. The results show a higher performance of the proposed method with a reduced computation time.

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Section: 5-template Matchingmentioning

confidence: 99%