28 April 2010 Multimodal eye recognition
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Abstract
Multimodal biometrics use more than one means of biometric identification to achieve higher recognition accuracy, since sometimes a unimodal biometric is not good enough used to do identification and classification. In this paper, we proposed a multimodal eye recognition system, which can obtain both iris and sclera patterns from one color eye image. Gabor filter and 1-D Log-Gabor filter algorithms have been applied as the iris recognition algorithms. In sclera recognition, we introduced automatic sclera segmentation, sclera pattern enhancement, sclera pattern template generation, and sclera pattern matching. We applied kernelbased matching score fusion to improve the performance of the eye recognition system. The experimental results show that the proposed eye recognition method can achieve better performance compared to unimodal biometric identification, and the accuracy of our proposed kernel-based matching score fusion method is higher than two classic linear matching score fusion methods: Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).
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Zhi Zhou, Yingzi Du, N. Luke Thomas, Edward J. Delp, "Multimodal eye recognition", Proc. SPIE 7708, Mobile Multimedia/Image Processing, Security, and Applications 2010, 770806 (28 April 2010); doi: 10.1117/12.849731; https://doi.org/10.1117/12.849731
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