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Titlebook: Biometric Recognition; 17th Chinese Confere Wei Jia,Wenxiong Kang,Jun Wang Conference proceedings 2023 The Editor(s) (if applicable) and Th

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樓主: patch-test
21#
發(fā)表于 2025-3-25 05:58:52 | 只看該作者
A Comparative Study on?Canonical Correlation Analysis-Based Multi-feature Fusion for?Palmprint Recogly, many existing methods have shown relatively satisfying performance, but there are still several problems such as the limited patterns extracted by single feature extraction approach and the huge gap between hand-crafted feature-based approaches and deep learning feature-based approaches. To this
22#
發(fā)表于 2025-3-25 09:37:44 | 只看該作者
23#
發(fā)表于 2025-3-25 13:47:39 | 只看該作者
24#
發(fā)表于 2025-3-25 16:16:05 | 只看該作者
25#
發(fā)表于 2025-3-25 20:22:58 | 只看該作者
Cross-Sensor Fingerprint Recognition Based on Style Transfer Network and Score Fusionsystemic deformation and the different imaging style. Most of the existing fingerprint recognition methods fail to consider the problem of cross-sensor fingerprint verification. This paper proposes a cross-sensor fingerprint recognition system based on style transfer and score fusion. The method use
26#
發(fā)表于 2025-3-26 01:48:44 | 只看該作者
Sparse Coding of?Deep Residual Descriptors for?Vein Recognitionunderlying issues such as uneven illumination, low contrast, and sparse patterns with high inter-class similarities make the traditional vein recognition systems based on hand-engineered features unreliable. To address the difficulty of direct training or fine-tuning a CNN with existing small-scale
27#
發(fā)表于 2025-3-26 05:57:51 | 只看該作者
MultiBioGM: A Hand Multimodal Biometric Model Combining Texture Prior Knowledge to?Enhance Generaliztional machine learning or deep learning have been proposed. However, the generalization ability of these methods is not satisfying due to the different entities, backgrounds, and sensors. In this paper, based on the three modalities of fingerprint, fingervein, and palmprint, the texture prior knowl
28#
發(fā)表于 2025-3-26 11:27:37 | 只看該作者
RRFAE-Net: Robust RGB-D Facial Age Estimation Networkntity-related representation. However, most existing facial age estimation methods usually extract age features from the RGB images, making them sensitive to the gender, race, pose and illumination changes. In this paper, we propose an end-to-end multi-feature integrated network for robust RGB-D fac
29#
發(fā)表于 2025-3-26 13:30:28 | 只看該作者
30#
發(fā)表于 2025-3-26 19:03:41 | 只看該作者
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