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- Title
基于轻量级卷积神经网络的 小样本虹膜图像分割.
- Authors
霍 光; 林大为; 刘元宁; 朱晓冬2.; 袁 梦
- Abstract
Aiming at the problem that complex segmentation networks could not converge on small sample iris datasets, we proposed an iris segmentation model based on lightweight convolutional neural network. Firstly, the model used a feature extraction module based on depth-wise separable convolution to extract iris image features, which could significantly reduce model parameters while maintaining segmentation accuracy. Secondly, an efficient attention mechanism module was introduced between the encoder and the decoder, which could effectively obtain rich context information and improve the discriminability of iris region pixels. Finally, the experimental results on the iris database UBIRIS.V2 show that the proposed method not only has significant performance advantages on small sample databases, but also has high segmentation accuracy on large sample databases.
- Subjects
CONVOLUTIONAL neural networks; FEATURE extraction; DATABASES; IMAGE segmentation; IRIS recognition; DEEP learning
- Publication
Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban), 2023, Vol 61, Issue 3, p583
- ISSN
1671-5489
- Publication type
Article
- DOI
10.13413/j.cnki.jdxblxb.2022078