publications.bib

@InProceedings{10.1007/978-3-031-49601-1_13,
author="Singh, Abhishek Pratap
and Sadhya, Debanjan
and Rathore, Santosh Singh",
editor="Goyal, Vikram
and Kumar, Naveen
and Bhowmick, Sourav S.
and Goyal, Pawan
and Goyal, Navneet
and Kumar, Dhruv",
title="Fast Similarity Search in Large-Scale Iris Databases Using High-Dimensional Hashing",
booktitle="Big Data and Artificial Intelligence",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="187--200",
abstract="Iris recognition is a widely used biometric identification technology that relies on the accurate and efficient matching of iris images. However, fast matching in large databases poses a significant challenge due to the increasing search time for a given query. To address this problem, this paper proposes an end-to-end hashing framework for iris recognition tasks based on the DenseFly algorithm. The presented approach utilizes a deep convolutional neural network to extract features from iris images and then applies hashing to map the features into compact binary codes. This process enables efficient retrieval of the query iris templates by reducing the whole search space. To evaluate and compare the proposed method with the existing IHashNet approach, we conduct experiments on three publicly available iris datasets namely CASIA-Irisv4-Thousand, UBIRIS.v2 and CASIA-Irisv4-Lamp. Our simulation results demonstrate that the proposed method outperforms IHashNet in terms of retrieval accuracy and equal error rate (EER). Furthermore, our method achieves significantly lower query time over all the datasets, thereby vindicating its usage over large iris datasets.",
isbn="978-3-031-49601-1"
}