Combining Multiple Color Components for Efficient Visible Spectral Iris Localization

Wang, Xue; He, Yuqing; Pei, Kuo; Liang, Mengmeng; He, Jingxi

doi:10.1007/978-3-319-46654-5_40

Cited by 2 publications

(1 citation statement)

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“…It simplifies the structure of DeepLab for the iris segmentation task to better match the segmentation task in IR. The experiments were conducted on the visible spectrum iris dataset [113]. The final experimental segmentation accuracy is 97.5%.…”

Section: Segmentation Based On Other Neural Networkmentioning

confidence: 99%

Deep Learning for Iris Recognition: A Review

Yin¹,

He²,

Zhang³

et al. 2023

Preprint

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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.

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Section: Segmentation Based On Other Neural Networkmentioning

confidence: 99%