Multi-iris indexing and retrieval: Fusion strategies for bloom filter-based search structures

We present a multi-iris indexing system for efficient and accurate large-scale identification. The system is based on Bloom filters and binary search trees. We describe and empirically evaluate several possible information fusion strategies for the system. Those experiments are performed using a combination of several publicly available datasets; the proposed system is tested in an open-set identification scenario consisting of 6,000 genuine and 100,000 impostor transactions. The system maintains the near-optimal biometric performance of an iris-code, score fusion based baseline system, while reducing the required lookup workload to less than 1% thereof.