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Boosting ordinal features for accurate and fast iris recognition

11 years 17 days ago
Boosting ordinal features for accurate and fast iris recognition
In this paper, we present a novel iris recognition method based on learned ordinal features.Firstly, taking full advantages of the properties of iris textures, a new iris representation method based on regional ordinal measure encoding is presented, which provides an over-complete iris feature set for learning. Secondly, a novel Similarity Oriented Boosting (SOBoost) algorithm is proposed to train an efficient and stable classifier with a small set of features. Compared with Adaboost, SOBoost is advantageous in that it operates on similarity oriented training samples, and therefore provides a better way for boosting strong classifiers. Finally, the well-known cascade architecture is adopted to reorganize the learned SOBoost classifier into a 'cascade', by which the searching ability of iris recognition towards large-scale deployments is greatly enhanced. Extensive experiments on two challenging iris image databases demonstrate that the proposed method achieves state-ofthe-ar...
Zhaofeng He, Zhenan Sun, Tieniu Tan, Xianchao Qiu,
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2008
Where CVPR
Authors Zhaofeng He, Zhenan Sun, Tieniu Tan, Xianchao Qiu, Cheng Zhong, Wenbo Dong
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