28 May 2013 Feature quality-based multimodal unconstrained eye recognition
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Abstract
Iris recognition has been tested to the most accurate biometrics using high resolution near infrared images. However, it does not work well under visible wavelength illumination. Sclera recognition, however, has been shown to achieve reasonable recognition accuracy under visible wavelengths. Combining iris and sclera recognition together can achieve better recognition accuracy. However, image quality can significantly affect the recognition accuracy. Moreover, in unconstrained situations, the acquired eye images may not be frontally facing. In this research, we proposed a feature quality-based multimodal unconstrained eye recognition method that combine the respective strengths of iris recognition and sclera recognition for human identification and can work with frontal and off-angle eye images. The research results show that the proposed method is very promising.
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Zhi Zhou, Eliza Y. Du, Yong Lin, N. Luke Thomas, Craig Belcher, Edward J. Delp, "Feature quality-based multimodal unconstrained eye recognition", Proc. SPIE 8755, Mobile Multimedia/Image Processing, Security, and Applications 2013, 87550J (28 May 2013); doi: 10.1117/12.2018664; https://doi.org/10.1117/12.2018664
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