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ICBA
2004
Springer

Improving Iris Recognition Accuracy via Cascaded Classifiers

9 years 18 days ago
Improving Iris Recognition Accuracy via Cascaded Classifiers
As a reliable approach to human identification, iris recognition has received increasing attention in recent years. In the literature of iris recognition, local feature of image details has been verified as an efficient iris signature. But measurements from minutiae are easily affected by noises, which greatly limits the system’s accuracy. When the matching score between two intra-class iris images is near the local feature based classifier’s (LFC) decision boundary, the poor quality iris images are usually involved in matching. Then a novel iris blob matching algorithm is resorted to make the recognition decision which is more robust than the LFC in the noisy environment. The extensive experimental results demonstrate that the cascading scheme significantly outperforms individual classifier in terms of accuracy and robustness.
Zhenan Sun, Yunhong Wang, Tieniu Tan, Jiali Cui
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where ICBA
Authors Zhenan Sun, Yunhong Wang, Tieniu Tan, Jiali Cui
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