Open Access Open Access  Restricted Access Subscription or Fee Access

Iris Image Identification Using Gray Level Co-occurrence Features and FMT-Based Normalization

Behnaz Mazaheri, Hossein Pourghassem


Iris is one of the best biometrics features for human identification and verification. In this paper, the proposed framework is consisting of iris image segmentation, normalization, feature extraction and classification. We use canny edge detector to generate an edge map and Hough transform to segment iris image. The next stage is to transform on the iris regions so that they have the same dimensions. The dimensional inconsistency between eye images are due to the stretching of the iris caused by pupil dilation from varying levels of illumination, varying imaging distance, rotation of the camera, head incline, and rotation of the eye within the eye socket. We apply Fourier Mellin Transform (FMT) to make the normalized images that are invariant to rotation and translation. Also, using Gray Level Co-occurrence Matrix (GLCM), statistical features of the normalized images are extracted. The proposed algorithm is evaluated on standard CASIA database. A multilayer perceptron classifier obtains an accuracy rate of 98.76% for iris image classification problem. The obtained results show that our proposed algorithm is more efficient than other common approach in real and operational conditions.


Iris image classification, Feature Extraction, Fourier-Mellin Transform, Gray-Level Co-occurrence Matrix, iris identification, Multilayer Perceptron.

Full Text:


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information. Also: DOI is paid service which provided by a third party. We never mentioned that we go for this for our any journal. However, journal have no objection if author go directly for this paid DOI service.