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Methods for accuracy-preserving acceleration of large-scale comparisons in CPU-based iris recognition systems

Methods for accuracy-preserving acceleration of large-scale comparisons in CPU-based iris recognition systems

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To confirm an individual's identity accurately and reliably iris recognition systems analyse the texture that is visible in the iris of the eye. The rich random pattern of the iris constitutes a powerful biometric characteristic suitable for biometric identification in large-scale deployments. Identification attempts or deduplication checks require an exhaustive one-to-many comparison. Hence, for large-scale biometric databases with millions of enrollees, the time required for a biometric identification is expected to significantly increase. In this study, the authors analyse techniques to accelerate Hamming distance-based comparisons of binary biometric reference data, i.e. iris-codes, in large-scale iris recognition systems, which preserve the biometric performance. The focus is put on software-based optimisations, an efficient two-step iris-code alignment process referred to as TripleA, and a combination thereof. Benchmarking the throughput and identifying potential bottlenecks of a portable commodity hardware-based iris recognition system is of particular interest. Based on the conducted experiments the authors point out practical boundaries of large-scale comparisons in central processing unit-based iris recognition systems, bridging the gap between the fields of iris recognition and software design.

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