With the widespread use of biometric technology in many applications, iris recognition techniques on mobile devices have been more commercialized recently. Some of these techniques include the Pier 2.3 system and the HIIDE Series 4. When working with mobile devices, the shaking movements of a given user's hand can sometimes cause motion-blurred iris images, which can reduce iris recognition accuracy. We propose a new method of restoring these kinds of motion-blurred iris images. We present three improvements over previous works: (1) We estimated the direction and magnitude of motion, based on the corneal specular reflection of the pupil. (2) The point spread function (PSF) was adaptively modeled based on the estimated motion information. (3) The restoration of motion-blurred iris images was performed with the filter parameters that were determined in terms of increasing the accuracy of iris recognition. Experimental results showed that the equal error rate (EER) without the proposed motion-deblurring method was 1.538% and that with the proposed method was 0.962%. Consequently, the EER was reduced as much as 0.576% (=1.538-0.962%), and results showed that the accuracy of iris recognition with the proposed method was superior to results with conventional motion-deblurring methods.
Byung Jun Kang,
Kang Ryoung Park,
"Restoration of motion-blurred iris images on mobile iris recognition devices," Optical Engineering 47(11), 117202 (1 November 2008). https://doi.org/10.1117/1.3028280