Title :
Quality fusion based multimodal eye recognition
Author :
Zhou, Zhi ; Du, Eliza Yingzi ; Belcher, Craig ; Thomas, N. Luke ; Delp, Edward J.
Author_Institution :
Electr. & Comput. Eng. Dept., Indiana Univ.-Purdue Univ. Indianapolis, Indianapolis, IN, USA
Abstract :
Multimodal eye recognition can improve the biometric systems recognition accuracy by combining iris and sclera recognition. However, poor quality images can significantly affect the system performance. In this paper, we proposed a quality fusion based multimodal eye recognition. Our quality measure evaluated the entire eye image quality, iris area quality, and sclera area quality. The experimental results show that our overall iris and sclera quality scores are highly correlated to recognition accuracy, and our quality fusion based eye recognition can improve and predict the performance of eye recognition systems.
Keywords :
image fusion; iris recognition; biometric system recognition accuracy; eye image quality; image quality measure; iris area quality; iris quality scores; iris recognition; quality fusion based multimodal eye recognition; sclera area quality; sclera quality scores; sclera recognition; system performance; Accuracy; Area measurement; Databases; Feature extraction; Image recognition; Image segmentation; Iris recognition; Eye recognition; Iris recognition; Quality fusion; Quality measure; Sclera recognition;
Conference_Titel :
Systems, Man, and Cybernetics (SMC), 2012 IEEE International Conference on
Conference_Location :
Seoul
Print_ISBN :
978-1-4673-1713-9
Electronic_ISBN :
978-1-4673-1712-2
DOI :
10.1109/ICSMC.2012.6377912
