Abstract:Many iris recognition systems use filters to extract information about the texture of an iris image. In the Daugman-style approach, the filter output is mapped to a binary iris code. The normalized Ha...Many iris recognition systems use filters to extract information about the texture of an iris image. In the Daugman-style approach, the filter output is mapped to a binary iris code. The normalized Hamming distance between two iris codes is computed and decisions about the identity of a person are based on the computed distance. The normalized Hamming distance weights all bits in an iris code equally. However, this work presents experimental evidence that all the bits in an iris code are not equally useful. Some bits are more consistent than others. An explanation of the cause of this discrepancy is given. Different regions of the iris are compared to evaluate their relative consistency. Finally, this paper investigates the theoretical impact of consistent and inconsistent bits on the false reject rate of an iris recognition system.Read More
Publication Year: 2007
Publication Date: 2007-09-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 32
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Abstract: Many iris recognition systems use filters to extract information about the texture of an iris image. In the Daugman-style approach, the filter output is mapped to a binary iris code. The normalized Hamming distance between two iris codes is computed and decisions about the identity of a person are based on the computed distance. The normalized Hamming distance weights all bits in an iris code equally. However, this work presents experimental evidence that all the bits in an iris code are not equally useful. Some bits are more consistent than others. An explanation of the cause of this discrepancy is given. Different regions of the iris are compared to evaluate their relative consistency. Finally, this paper investigates the theoretical impact of consistent and inconsistent bits on the false reject rate of an iris recognition system.