Biometrics: Personal Identification in Networked Society
Biometrics: Personal Identification in Networked Society
Comments on the CASIA version 1.0 Iris Data Set
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICCI '06 Proceedings of the 2006 5th IEEE International Conference on Cognitive Informatics - Volume 02
Robust and fast assessment of iris image quality
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
A human identification technique using images of the iris andwavelet transform
IEEE Transactions on Signal Processing
New Methods in Iris Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Composing morphological filters
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Agent-based image iris segmentation and multipleviews boundary refining
BTAS'09 Proceedings of the 3rd IEEE international conference on Biometrics: Theory, applications and systems
Neural-based iterative approach for iris detection in iris recognition systems
CISDA'09 Proceedings of the Second IEEE international conference on Computational intelligence for security and defense applications
Iris recognition based on elastic graph matching and Gabor wavelets
Computer Vision and Image Understanding
Iris localization in frontal eye images for less constrained iris recognition systems
Digital Signal Processing
International Journal of Computer Applications in Technology
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The paper presents an innovative algorithm for the segmentation of the iris in noisy images, with boundaries regularization and the removal of the possible existing reflections. In particular, the method aims to extract the iris pattern from the eye image acquired at the visible wavelength, in an uncontrolled environment where reflections and occlusions can also be present, on-the-move and at variable distance. The method achieves the iris segmentation by the following three main steps. The first step locates the centers of the pupil and the iris in the input image. Then two image strips containing the iris boundaries are extracted and linearizated. The last step locates the iris boundary points in the strips and it performs a regularization operation by achieving the exclusion of the outliers and the interpolation of missing points. The obtained curves are then converted into the original image space in order to produce a first segmentation output. Occlusions such as reflections and eyelashes are then identified and removed from the final area of the segmentation. Results indicate that the presented approach is effective and suitable to deal with the iris acquisition in noisy environments. The proposed algorithm ranked seventh in the international Noisy Iris Challenge Evaluation (NICE.I).