A Deep Learning Based Approach to Iris Sensor Identification

Zabin, Ananya; Bourlai, Thirimachos

doi:10.1109/asonam49781.2020.9381354

Cited by 4 publications

(2 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is the champion of ILSVRC2012 and a landmark network in the whole field of deep learning. [58,59] use AlexNet to perform identification tasks of IR. [59] uses high-quality iris images selected from public datasets to train AlexNet, rather than all images in the dataset.…”

Section: Transfer Learning-based Methodsmentioning

confidence: 99%

“…[58,59] use AlexNet to perform identification tasks of IR. [59] uses high-quality iris images selected from public datasets to train AlexNet, rather than all images in the dataset. The image spatial quality evaluator evaluates the quality score of the iris images, and images with high scores are involved in the training process.…”

Section: Transfer Learning-based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning for Iris Recognition: A Review

Yin¹,

He²,

Zhang³

et al. 2023

Preprint

View full text Add to dashboard Cite

Iris recognition is a secure biometric technology known for its stability and privacy. With no two irises being identical and little change throughout a person's lifetime, iris recognition is considered more reliable and less susceptible to external factors than other biometric recognition methods. Unlike traditional machine learning-based iris recognition methods, deep learning technology does not rely on feature engineering and boasts excellent performance. This paper collects 120 relevant papers to summarize the development of iris recognition based on deep learning. We first introduce the background of iris recognition and the motivation and contribution of this survey. Then, we present the common datasets widely used in iris recognition. After that, we summarize the key tasks involved in the process of iris recognition based on deep learning technology, including identification, segmentation, presentation attack detection, and localization. Finally, we discuss the challenges and potential development of iris recognition. This review provides a comprehensive sight of the research of iris recognition based on deep learning.

show abstract

Section: Transfer Learning-based Methodsmentioning

confidence: 99%