Ying Chen
Nanchang Hangkong University
33 Papers
32 Citations
Ying Chen is an academic researcher from Nanchang Hangkong University. The author has contributed to research in topics: Iris recognition & Computer science. The author has an hindex of 6, co-authored 26 publications.
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Papers
Accurate iris segmentation and recognition using an end-to-end unified framework based on MADNet and DSANet
Ying Chen,Huimin Gan,Huiling Chen,Yugang Zeng,Liang-jun Xu,Ali Heidari,Xiaodong Zhu,Yuanning Liu +7 more
TL;DR: Zhang et al. as mentioned in this paper proposed an end-to-end unified framework based on deep learning that does not include normalization in order to achieve improved accuracy in iris segmentation and recognition.
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An Adaptive CNNs Technology for Robust Iris Segmentation
TL;DR: An architecture based on CNNs combined with dense blocks for iris segmentation, referred to as a dense-fully convolutional network (DFCN), and adopt some popular optimizer methods, such as batch normalization (BN) and dropout.
LDNNET: Towards Robust Classification of Lung Nodule and Cancer Using Lung Dense Neural Network
TL;DR: LDNNET as mentioned in this paper is an adaptive architecture based on convnets combining softmax classifier which is utilized to alleviate the problems of training deep convnets, which adopts DenseBlock, batch normalization (BN) and dropout to cope with these problems.
Efficient iris recognition based on optimal subfeature selection and weighted subregion fusion.
TL;DR: Three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity.
DADCNet: Dual attention densely connected network for more accurate real iris region segmentation
TL;DR: Results show that the proposed DADCNet achieves state‐of‐the‐art performance and that the mask image obtained after DADcNet segmentation can replace the published corresponding GT image.
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