ThirdEye: Triplet Based Iris Recognition without Normalization
This work addresses iris recognition systems by showing that normalization may be less critical in constrained environments, but it is incremental as it builds on existing deep learning methods.
The paper tackled the problem of whether normalization is necessary in iris recognition by developing ThirdEye, a triplet-based CNN system that uses segmented images without normalization, achieving equal error rates of 1.32%, 9.20%, and 0.59% on ND-0405, UbirisV2, and IITD datasets, with improvement on IITD but slightly worse performance on the others.
Most iris recognition pipelines involve three stages: segmenting into iris/non-iris pixels, normalization the iris region to a fixed area, and extracting relevant features for comparison. Given recent advances in deep learning it is prudent to ask which stages are required for accurate iris recognition. Lojez et al. (IWBF 2019) recently concluded that the segmentation stage is still crucial for good accuracy.We ask if normalization is beneficial? Towards answering this question, we develop a new iris recognition system called ThirdEye based on triplet convolutional neural networks (Schroff et al., ICCV 2015). ThirdEye directly uses segmented images without normalization. We observe equal error rates of 1.32%, 9.20%, and 0.59% on the ND-0405, UbirisV2, and IITD datasets respectively. For IITD, the most constrained dataset, this improves on the best prior work. However, for ND-0405 and UbirisV2,our equal error rate is slightly worse than prior systems. Our concluding hypothesis is that normalization is more important for less constrained environments.