H
Haiqing Li
Researcher at Chinese Academy of Sciences
Publications - 21
Citations - 1179
Haiqing Li is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Iris recognition & Feature extraction. The author has an hindex of 12, co-authored 21 publications receiving 801 citations.
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Proceedings ArticleDOI
Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-free Approach
TL;DR: The proposed method leverages Fully Convolutional Network (FCN) to generate fix-sized spatial feature maps such that pixel-level features are consistent and can decrease the similarity of coupled images from different persons and increase that from the same person.
Proceedings ArticleDOI
Accurate iris segmentation in non-cooperative environments using fully convolutional networks
TL;DR: Experimental results show that MFCNs are more robust than HCNNs to noises, and can greatly improve the current state-of-the-arts by 25.62% and 13.24% on the UBIRIS.v2 and CASIA.v4-distance databases, respectively.
Journal ArticleDOI
DeepIris: Learning Pairwise Filter Bank for Heterogeneous Iris Verification
TL;DR: DeepIris is a novel solution to iris recognition which learns relational features to measure the similarity between pairs of iris images based on convolutional neural networks, and EER of heterogeneous iris verification is reduced by 90% using DeepIris compared to traditional methods.
Journal ArticleDOI
Deep Feature Fusion for Iris and Periocular Biometrics on Mobile Devices
TL;DR: A deep feature fusion network that exploits the complementary information presented in iris and periocular regions to enhance the performance of mobile identification and requires much fewer storage spaces and computational resources than general CNNs.
Posted Content
Deep Spatial Feature Reconstruction for Partial Person Re-identification: Alignment-Free Approach
TL;DR: Wang et al. as discussed by the authors proposed a Deep Spatial Feature Reconstruction (DSR) method, which exploits the reconstructing error from popular dictionary learning models to calculate the similarity between different spatial feature maps.