IHashNet: Iris Hashing Network based on efficient multi-index hashing

TL;DR

This paper introduces IHashNet, an end-to-end iris indexing method that uses deep features and binary hashing to improve search efficiency in large biometric databases.

Contribution

It proposes a novel iris indexing scheme combining deep feature extraction, binarization, and a new loss function for improved multi-index hashing compatibility.

Findings

Effective iris indexing demonstrated on four datasets.

Improved retrieval accuracy with binary hashing.

First end-to-end iris indexing structure proposed.

Abstract

Massive biometric deployments are pervasive in today's world. But despite the high accuracy of biometric systems, their computational efficiency degrades drastically with an increase in the database size. Thus, it is essential to index them. An ideal indexing scheme needs to generate codes that preserve the intra-subject similarity as well as inter-subject dissimilarity. Here, in this paper, we propose an iris indexing scheme using real-valued deep iris features binarized to iris bar codes (IBC) compatible with the indexing structure. Firstly, for extracting robust iris features, we have designed a network utilizing the domain knowledge of ordinal filtering and learning their nonlinear combinations. Later these real-valued features are binarized. Finally, for indexing the iris dataset, we have proposed a loss that can transform the binary feature into an improved feature compatible with the Multi-Index Hashing scheme. This loss function ensures the hamming distance equally distributed among all the contiguous disjoint sub-strings. To the best of our knowledge, this is the first work in the iris indexing domain that presents an end-to-end iris indexing structure. Experimental results on four datasets are presented to depict the efficacy of the proposed approach.