Hash-based space partitioning approach to iris biometric data indexing

Ahmed, Tauheed; Sarma, Monalisa

doi:10.1016/j.eswa.2019.05.026

Cited by 14 publications

(15 citation statements)

References 24 publications

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“…Further, for analyzing the performance of iris indexing, hit-rate and penetration rate have been used. For details on performance parameters refer [5].…”

Section: Methodsmentioning

confidence: 99%

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

Singh¹,

Vashist²,

Gaurav³

et al. 2020

Preprint

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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 intersubject 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 M com loss that can transform the binary feature into an improved feature compatible with Multi-Index Hashing scheme. This M com 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.

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“…Further, for analyzing the performance of iris indexing, hit-rate and penetration rate have been used. For details on performance parameters refer [5].…”

Section: Methodsmentioning

confidence: 99%

“…Recently, Rathgeb et al [10] study the concept of a bloom filter for indexing iris dataset giving promising results. To the best of our knowledge, the work done by Ahmed et al [5] is the current state-of-the-art in iris indexing. In their work, they have proposed an indexing strategy based on hyper-planes.…”

Section: Introductionmentioning

confidence: 99%

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

Singh¹,

Vashist²,

Gaurav³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The experimental result showed that the proposed distributed scheme had great potential to search images in large datasets with multiple nodes. Ahmed and Sarma [39] detected that the accuracy of a system degraded with the increase in the size of the database, therefore an indexing approach was designed to deal with the feature deviation under noise. Considering the retrieval task, the proposed indexing approach gave higher hit rate than existing approaches, even at low penetration rate.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Case Retrieval Algorithm for Agricultural Case-Based Reasoning Systems, with Consideration of Case Base Maintenance

et al. 2020

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Case-based reasoning has considerable potential to model decision support systems for smart agriculture, assisting farmers in managing farming operations. However, with the explosive amount of sensing data, these systems may achieve poor performance in knowledge management like case retrieval and case base maintenance. Typical approaches of case retrieval have to traverse all past cases for matching similar ones, leading to low efficiency. Thus, a new case retrieval algorithm for agricultural case-based reasoning systems is proposed in this paper. At the initial stage, an association table is constructed, containing the relationships between all past cases. Afterwards, attributes of a new case are compared with an entry case. According to the similarity measurement, associated similar or dissimilar cases are then compared preferentially, instead of traversing the whole case base. The association of the new case is generated through case retrieval and added in the association table at the step of case retention. The association table is also updated when a closer relationship is detected. The experiment result demonstrates that our proposal enables rapid case retrieval with promising accuracy by comparing a fewer number of past cases. Thus, the retrieval efficiency of our proposal outperforms typical approaches.

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“…These advantages make it obtained widely applied in the computer vision task, such as image clustering [5], image retrieval [6] and multi-view learning [7] etc. Nowadays, binary coding methods have been well investigated in many fields.…”

Section: Introductionmentioning

confidence: 99%