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Erschienen in: Pattern Recognition and Image Analysis 1/2021

01.01.2021 | APPLIED PROBLEMS

Multi-Source Heterogeneous Iris Recognition Using Stacked Convolutional Deep Belief Networks-Deep Belief Network Model

verfasst von: Guang Huo, Qi Zhang, Yangrui Zhang, Yuanning Liu, Huan Guo, Wenyu Li

Erschienen in: Pattern Recognition and Image Analysis | Ausgabe 1/2021

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Abstract

With the development of iris recognition technology, sensors of iris images acquisition are being constantly developed and updated. Re-register users every time a new sensor is deployed is time-consuming and complicated, especially in applications with large-scale registered users. Therefore, it is a challenging problem to choose the common recognition model which is effective for multi-source heterogeneous iris recognition(MSH-IR). The paper proposes a efficient neural network model of stacked Convolutional Deep Belief Networks-Deep Belief Network (CDBNs-DBN) for MSH-IR. The main improvements are two parts: firstly, this model uses the region-by-region extraction method and positions the convolution kernel through the offset of the hidden layer to locate the effective local texture feature structure. Secondly, the model uses DBN as a classifier in order to reduce the reconstruction error through the negative feedback mechanism of the auto-encoder. Experimental results have been implemented on publicly available IIT Delhi iris database, which is captured by three different iris captured sensors. Experiments shows the model performs strong robustness performance and recognition ability.

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Metadaten
Titel
Multi-Source Heterogeneous Iris Recognition Using Stacked Convolutional Deep Belief Networks-Deep Belief Network Model
verfasst von
Guang Huo
Qi Zhang
Yangrui Zhang
Yuanning Liu
Huan Guo
Wenyu Li
Publikationsdatum
01.01.2021
Verlag
Pleiades Publishing
Erschienen in
Pattern Recognition and Image Analysis / Ausgabe 1/2021
Print ISSN: 1054-6618
Elektronische ISSN: 1555-6212
DOI
https://doi.org/10.1134/S1054661821010119

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