25 August 2006 High-resolution iris image reconstruction from low-resolution imagery
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Abstract
We investigate the use of a novel multi-lens imaging system in the context of biometric identification, and more specifically, for iris recognition. Multi-lenslet cameras offer a number of significant advantages over standard single-lens camera systems, including thin form-factor and wide angle of view. By using appropriate lenslet spacing relative to the detector pixel pitch, the resulting ensemble of images implicitly contains subject information at higher spatial frequencies than those present in a single image. Additionally, a multi-lenslet approach enables the use of observational diversity, including phase, polarization, neutral density, and wavelength diversities. For example, post-processing multiple observations taken with differing neutral density filters yields an image having an extended dynamic range. Our research group has developed several multi-lens camera prototypes for the investigation of such diversities. In this paper, we present techniques for computing a high-resolution reconstructed image from an ensemble of low-resolution images containing sub-pixel level displacements. The quality of a reconstructed image is measured by computing the Hamming distance between the Daugman4 iris code of a conventional reference iris image, and the iris code of a corresponding reconstructed image. We present numerical results concerning the effect of noise and defocus blur in the reconstruction process using simulated data and report preliminary work on the reconstruction of actual iris data obtained with our camera prototypes.
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R. Barnard, R. Barnard, V. P. Pauca, V. P. Pauca, T. C. Torgersen, T. C. Torgersen, R. J. Plemmons, R. J. Plemmons, S. Prasad, S. Prasad, J. van der Gracht, J. van der Gracht, J. Nagy, J. Nagy, J. Chung, J. Chung, G. Behrmann, G. Behrmann, S. Mathews, S. Mathews, M. Mirotznik, M. Mirotznik, } "High-resolution iris image reconstruction from low-resolution imagery", Proc. SPIE 6313, Advanced Signal Processing Algorithms, Architectures, and Implementations XVI, 63130D (25 August 2006); doi: 10.1117/12.681930; https://doi.org/10.1117/12.681930
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