Shuyi Li
University of Macau
33 Papers
10 Citations
Shuyi Li is an academic researcher from University of Macau. The author has contributed to research in topics: Computer science & Feature extraction. The author has an hindex of 5, co-authored 13 publications. Previous affiliations of Shuyi Li include Civil Aviation University of China.
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Papers
Local discriminant coding based convolutional feature representation for multimodal finger recognition
TL;DR: A discriminant local coding based convolutional neural network (LC-CNN) is proposed for multimodal finger recognition by fusing fingerprint, finger-vein, and finger-knuckle-print traits to extract deeper tri-modal finger features.
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Joint discriminative feature learning for multimodal finger recognition
TL;DR: This paper proposed a joint discriminative feature learning (JDFL) framework for multimodal finger recognition by combining finger vein (FV) and finger knuckle print (FKP) patterns, which has a better recognition performance than state-of-the-art finger recognition methods.
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Joint Discriminative Sparse Coding for Robust Hand-Based Multimodal Recognition
TL;DR: This paper proposes a simple yet effective supervised multimodal feature learning method, called joint discriminative sparse coding (JDSC), which is applied for hand-based multi-modality recognition including finger-vein and finger-knuckle-print fusion, palm-veIn and palm print fusion, as well as palm-VEin and dorsal-hand-vesin fusion.
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Graph Fusion for Finger Multimodal Biometrics
TL;DR: This paper provides two fusion frameworks to integrate the finger trimodal graph features together, the serial fusion and coding fusion, and shows that the proposed graph fusion recognition approach obtains a better and more effective recognition performance in finger biometrics.
Learning Compact Multirepresentation Feature Descriptor for Finger-Vein Recognition
TL;DR: A novel compact multi-representation feature descriptor (CMrFD) with visual and semantic consistency, for finger-vein feature representation, and experimental results demonstrate that the proposed method outperforms the state-of-the-art finger-VEin recognition methods.
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