Iris recognition using signal-level fusion of frames from video
IEEE Transactions on Information Forensics and Security - Special issue on electronic voting
Flexible image segmentation and quality assessment for real-time iris recognition
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
Local quality method for the iris image pattern
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Iris image deblurring based on refinement of point spread function
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
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In the field of biometrics, it has been reported that iris recognition techniques have shown high levels of accuracy because unique patterns of the human iris, which has very many degrees of freedom, are used. However, because conventional iris cameras have small depth-of-field (DOF) areas, input iris images can easily be blurred, which can lead to lower recognition performance, since iris patterns are transformed by the blurring caused by optical defocusing. To overcome these problems, an autofocusing camera can be used. However, this inevitably increases the cost, size, and complexity of the system. Therefore, we propose a new real-time iris image-restoration method, which can increase the camera's DOF without requiring any additional hardware. This paper presents five novelties as compared to previous works: (1) by excluding eyelash and eyelid regions, it is possible to obtain more accurate focus scores from input iris images; (2) the parameter of the point spread function (PSF) can be estimated in terms of camera optics and measured focus scores; therefore, parameter estimation is more accurate than it has been in previous research; (3) because the PSF parameter can be obtained by using a predetermined equation, iris image restoration can be done in real-time; (4) by using a constrained least square (CLS) restoration filter that considers noise, performance can be greatly enhanced; and (5) restoration accuracy can also be enhanced by estimating the weight value of the noise-regularization term of the CLS filter according to the amount of image blurring. Experimental results showed that iris recognition errors when using the proposed restoration method were greatly reduced as compared to those results achieved without restoration or those achieved using previous iris-restoration methods.