III WVC 2007 - Iris.sel.eesc.sc.usp.br - USP

WVC'2007 - III Workshop de Visão Computacional, 22 a 24 de Outubro de 2007, São José do Rio Preto, SP.The choice of the acceptance threshold will bedetermined by the applications needs, for instance, inhigh security applications where the avoidance of falsepositives is the major issue, a low acceptance thresholdis recommended. On the other hand for applicationswhere the inconvenience caused by a false negative isthe main concern, a comparatively higher acceptancethreshold may be appropriate.The system performance as a function of theacceptance value may be better observed on ROC(Receiver Operating Characteristics) plots. Figures 8, 9and 10 show the ROC curves for both methods,considering respectively all the image pairs, images withmore than 15% of occlusion and only images with lessthan 15% of occlusion.The closer the ROC curve stays to the horizontal andvertical axes the higher the associated performance.Figures 8, 9 and 10 show that, in terms of recognitionperformance, our method is considerably better than theBoles method for all levels of occlusion. The ROCcurves for Boles moves away from the horizontal andvertical axes faster than the proposed method as theocclusion increases.Figure 10. ROC curve for images with less than 15% ofocclusionA further analysis of the experimental results hasshown that the occurrence of false negatives is mostlyinfluenced by the difference of the pupil’s diameter inboth images being analyzed. This suggests that a moreelaborated image normalization algorithm maysignificantly improve the false negative rate.6. Final commentsFigure 8. ROC curve for all images in the data setThis work proposed an extension to Boles method inorder to make it robust against occlusion. Experimentsconducted on images from the Casia database for thesystem working in verification mode indicated that theproposal significantly improves the recognitionperformance particularly for images highly affected byocclusion.Certainly the efficiency of the proposed methoddepends on how accurately the occlusion areas aredelineated in the input image. In these experiments theareas of occlusion were determined manually for eachimage. A study of automatic methods for eyelids andeyelashes detection in conjunction with the proposedextension is planed for the continuation of this work.Daugman’s method [3] is the most widely acceptedbenchmark in the research of iris recognition. An earlierstudy [7] indicated that the superiority of Daugman’smethod over Boles’ method could be mainly credited tothe inability of Boles’ to deal with occlusion. A futurestudy is also planed aiming at verifying how close theproposed extension of Boles’ method comes toDaugman’s performance.7. ReferencesFigure 9. ROC curve only for images with more than 15%of occlusion[1] S. Nanavati, M. Thieme, R. Nanavati, Biometrics: IdentityVerification in a Networked World, John Wiley & Sohns,2002.288