Feature quality-based multimodal unconstrained eye recognition

Zhi Zhou; Eliza Y. Du; Yong Lin; N. Luke Thomas; Craig Belcher; Edward J. Delp

doi:10.1117/12.2018664

28 May 2013 Feature quality-based multimodal unconstrained eye recognition

Zhi Zhou, Eliza Y. Du, Yong Lin, N. Luke Thomas, Craig Belcher, Edward J. Delp

Proceedings Volume 8755, Mobile Multimedia/Image Processing, Security, and Applications 2013; 87550J (2013) https://doi.org/10.1117/12.2018664
Event: SPIE Defense, Security, and Sensing, 2013, Baltimore, Maryland, United States

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.

Citation Download Citation

Zhi Zhou, Eliza Y. Du, Yong Lin, N. Luke Thomas, Craig Belcher, and 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); https://doi.org/10.1117/12.2018664

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KEYWORDS

Iris recognition

Sclera

Eye

Image segmentation

Image quality

Image fusion

Calibration

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