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...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Zhaofeng He, Zhenan Sun, Tieniu Tan, Xianchao Qiu, Cheng Zhong, Wenbo Dong
• Keywords: Challenging Iris Image, Computer Vision, CVPR 2008, Iris Recognition Accuracy, Iris Representation Method, Iris Textures, Over-complete Iris Feature