Iris Recognition based on Convolutional Neural Networks and Multi-Index Hashing

• Maximilian Harrisson Universidade Federal Fluminense (UFF)
• Leandro S. de Araújo Universidade Federal Fluminense (UFF) https://orcid.org/0000-0002-3631-8761

DOI: https://doi.org/10.5753/sbbd_estendido.2026.249743

Resumo

Efficient iris recognition at scale requires indexing structures that can search large galleries without a linear scan. This work evaluates 7 feature extractors, with 5 pre-trained CNNs (VGG16, ResNet50, ConvNeXt-Tiny, MobileNetV2, InceptionV3) and 2 classical descriptors (SIFT, ORB), combined with median binarization and Multi-Index Hashing on the CASIA-Iris-Lamp dataset, containing 16212 images and 411 subjects. The experiments compare extractors under a fixed 32-bit segment parameterization, analyze the trade-off between hit rate and penetration rate as a function of the number of tables, and evaluate thermometer binarization as an alternative to median. CNN extractors achieve rank-1 identification rates near 99%, while SIFT and ORB reach only 10.60% and 51.45% despite similar hit rates, showing that hit rate alone does not reflect recognition quality. VGG16 offers the best trade-off, combining a rank-1 rate of 99.45% with a penetration rate of 18.02%. Thermometer binarization improves rank-1 but produces a penetration rate of 100% across all tested configurations, eliminating the indexing advantage of Multi-Index Hashing.

Palavras-chave: Iris Recognition, Information Retrieval, Multi-Index Hashing, Convolutional Neural Networks, Nearest Neighbors search

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