Feature extraction for different distances of visible reflection iris using multiscale sparse representation of local Radon transform

• Visible iris image is prune to investigate iris textures at different distances.
• Multiscale and sparse representation of local radon transform is proposed.
• Multiscale is used to reduce noise during down sampling of normalized iris.
• Sparse representation is used to generate compact information of iris features.
• The proposed combination is able to increase accuracy of iris recognition.

Iris recognition is a promising method by which to accurately identify a person. During the iris recognition stage, the features of the iris are extracted, including the unique, individual texture of the iris. The ability to extract the texture of the iris in non-cooperative environments from eye images captured at different distances, containing reflections, and under visible wavelength illumination will lead to increased iris recognition performance. A method that combined multiscale sparse representation of local Radon transform was proposed to down sample a normalized iris into different lengths of scales and different orientations of angles to form an iris feature vector. This research was tested using 1000 eye images from the UBIRIS.v2 database. The results showed that the proposed method performed better than existing methods when dealing with iris images captured at different distances.