IHashNet: Iris Hashing Network based on efficient multi-index hashing
This work aims to improve the computational efficiency of large-scale iris recognition systems by providing an end-to-end indexing solution, which is an incremental improvement for biometric system developers.
This paper addresses the computational inefficiency of large-scale biometric systems by proposing an iris indexing scheme called IHashNet. It generates binarized iris bar codes from deep iris features, which are then transformed to be compatible with a Multi-Index Hashing scheme, ensuring efficient indexing for massive iris databases.
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.