A Design of Iris Recognition System at a Distance

Iris recognition is a powerful biometrics for personal identification, but it is difficult to acquire good-quality iris images in real time. For making iris recognition more convenient to use, we design an iris recognition system at a distance about 3 meters. There are many key issues to design such a system, including iris image acquisition, human-machine-interface and image processing. In this paper, we respectively introduce how we deal with these problems and accomplish the engineering design. Experiments show that our system is convenient to use at the distance of 3 meters and the recognition rate is not worse than the state-of-the-art close-range systems. 1)Iris image acquisition: The human iris is very small and the required resolution for iris recognition is large, so it is diffi- cult to design the optical path for iris imaging at a distance. We carefully calculate the parameters of cameras, lens and illumi- nation intensity and elaborately select their types to set up the optical system. 2)Human-machine-interface: Because users are of differ- ent height, it is impossible for a single camera to cover so large range or for users to cooperate with the camera at a distance. We design a self-adaptive machine to automatically adapt to different people. Moreover, we use screens and audio signals to direct users to stand on the right position and give them mul- timedia feedback. All these devices are installed into a cabinet

[1]  Guodong Guo,et al.  A System for Automatic Iris Capturing , 2005 .

[2]  Tieniu Tan,et al.  Toward Accurate and Fast Iris Segmentation for Iris Biometrics , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Tieniu Tan,et al.  Ordinal Measures for Iris Recognition , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  James R. Matey,et al.  Iris on the Move: Acquisition of Images for Iris Recognition in Less Constrained Environments , 2006, Proceedings of the IEEE.

[5]  Yingzi Du,et al.  Feature information based quality measure for iris recognition , 2007, 2007 IEEE International Conference on Systems, Man and Cybernetics.

[6]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[7]  John Daugman,et al.  10.7 – How Iris Recognition Works , 2005 .

[8]  Ho Gi Jung,et al.  Non-intrusive Iris Image Capturing System Using Light Stripe Projection and Pan-Tilt-Zoom Camera , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Tieniu Tan,et al.  Self-adaptive iris image acquisition system , 2008, SPIE Defense + Commercial Sensing.

[10]  John Daugman How iris recognition works , 2004 .

[11]  Anil K. Jain,et al.  Localized Iris Image Quality Using 2-D Wavelets , 2006, ICB.

[12]  Yingzi Du,et al.  A Selective Feature Information Approach for Iris Image-Quality Measure , 2008, IEEE Transactions on Information Forensics and Security.

[13]  John Daugman,et al.  Probing the Uniqueness and Randomness of IrisCodes: Results From 200 Billion Iris Pair Comparisons , 2006, Proceedings of the IEEE.

[14]  Tieniu Tan,et al.  How to make iris recognition easier? , 2008, 2008 19th International Conference on Pattern Recognition.