Abstract
The traditional iris recognition systems require equal high quality human iris images. A cheap image acquisition system has difficulty in capturing equal high quality iris images. This paper describes a new feature representation method for iris recognition robust to noises. The disc-shaped iris image is first convolved with a low pass filter along the radial direction. Then, the radially smoothed iris image is decomposed in the angular direction using a one-dimensional continuous wavelet transform. Each decomposed one-dimensional waveform is approximated by an optimal piecewise linear curve connecting a small set of node points. The set of node points is used as a feature vector. The optimal approximation procedure reduces the feature vector size while maintaining recognition accuracy. The similarity between two iris images is measured by the normalized cross-correlation coefficients between optimal curves. The similarity between two iris images is estimated using mid-frequency bands. The rotation of one-dimensional signals due to the head tilt is estimated using the lowest frequency component. Experimentally we show the proposed method produces superb performance in iris recognition.
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Kim, J., Cho, S., Choi, J. et al. Iris Recognition Using Wavelet Features. The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology 38, 147–156 (2004). https://doi.org/10.1023/B:VLSI.0000040426.72253.b1
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DOI: https://doi.org/10.1023/B:VLSI.0000040426.72253.b1