Improving Iris Recognition Accuracy via Cascaded Classifiers  

Oleh:

Zhenan, Sun ; Yunhong, Wang ; Tieniu Tan ; Jiali Cui

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: IEEE Transactions on Systems, Man, and Cybernetics: Part C Applications and Reviews vol. 35 no. 3 (Aug. 2005), page 435-441.
Topik: Biometrics; Blob Matching; Cascaded Classifiers; Iris Recognition

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: II69.1 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

As a reliable approach to human identification, iris recognition has received increasing attention in recent years. The most distinguishing feature of an iris image comes from the fine spatial changes of the image structure. So iris pattern representation must characterize the local intensity variations in iris signals. However, the measurements from minutiae are easily affected by noise, such as occlusions by eyelids and eyelashes, iris localization error, nonlinear iris deformations, etc. This greatly limits the accuracy of iris recognition systems. In this paper, an elastic iris blob matching algorithm is proposed to overcome the limitations of local feature based classifiers (LFC). In addition, in order to recognize various iris images efficiently a novel cascading scheme is proposed to combine the LFC and an iris blob matcher. When the LFC is uncertain of its decision, poor quality iris images are usually involved in intra-class comparison. Then the iris blob matcher is resorted to determine the input iris' identity because it is capable of recognizing noisy images. Extensive experimental results demonstrate that the cascaded classifiers significantly improve the system's accuracy with negligible extra computational cost.

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