IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Texture Classification by Subsets of Local Binary Patterns
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
Face Description with Local Binary Patterns: Application to Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local feature extraction for iris recognition with automatic scale selection
Image and Vision Computing
The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-the-Move and At-a-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph matching iris image blocks with local binary pattern
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
UBIRIS: a noisy iris image database
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
IEEE Transactions on Circuits and Systems for Video Technology
The results of the NICE.II Iris biometrics competition
Pattern Recognition Letters
A review of information fusion techniques employed in iris recognition systems
International Journal of Advanced Intelligence Paradigms
Hi-index | 0.10 |
One of the most challenging issues in iris recognition is the design of techniques able to ensure high accuracy even in adverse conditions. This paper deals with an approach to iris matching based on the combination of local features: Linear Binary Patterns (LBP) and discriminable textons (BLOBs) are presently exploited. The techniques have been refined ad hoc, to allow the extraction of significant discriminative features, even with images captured in variable visible light conditions, and affected by noise due to distance/resolution or to scarce user collaboration (blurring, off-axis iris, occlusion by eyelashes and eyelids). The obtained results strongly motivate further investigations along this line, most of all the addition of more local features.