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Titlebook: Biometric Recognition; 16th Chinese Confere Weihong Deng,Jianjiang Feng,Zhaofeng He Conference proceedings 2022 The Editor(s) (if applicabl

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樓主: 小天使
11#
發(fā)表于 2025-3-23 13:34:29 | 只看該作者
SP-FVR: SuperPoint-Based Finger Vein Recognitionadopted keypoint descriptors may be insufficient in keypoint quantity and descriptor distinctiveness, since they are generally artificially-designed and sensitive to the poor quality of the finger vein images. To address the above-mentioned problem, an automatically-learned keypoint-based method uti
12#
發(fā)表于 2025-3-23 17:18:12 | 只看該作者
TransFinger: Transformer Based Finger Tri-modal Biometricsloring multi-modal biometric fusion recognition of fingers is very meaningful for improving the security and stability of finger feature expression. Due to the differences in the expression of different modal features, the interaction mode and fusion strategy between different modalities are the key
13#
發(fā)表于 2025-3-23 21:27:22 | 只看該作者
A Survey of?Domain Generalization-Based Face Anti-spoofingralization techniques have been widely exploited for adapting face anti-spoofing models to unseen testing scenarios. In this paper, we present a comprehensive survey on domain generalization-based face anti-spoofing methods. Specifically, we propose a taxonomy for existing methods and conduct a thor
14#
發(fā)表于 2025-3-24 01:14:18 | 只看該作者
15#
發(fā)表于 2025-3-24 02:32:44 | 只看該作者
16#
發(fā)表于 2025-3-24 10:22:58 | 只看該作者
17#
發(fā)表于 2025-3-24 11:12:48 | 只看該作者
18#
發(fā)表于 2025-3-24 16:05:21 | 只看該作者
The Language Question in Africa,pendent intellectual property rights. Biometric recognition technologies require increasing investment and targeted niche research if they are to play a more significant role in forensic science in the future.
19#
發(fā)表于 2025-3-24 21:27:26 | 只看該作者
Language Policy and Language Planningommonly used datasets and evaluation metrics, and also analyze the performance of existing methods to uncover key factors affecting the generalization performance. Finally, we conclude this survey with a forecast on promising future research directions.
20#
發(fā)表于 2025-3-24 23:22:03 | 只看該作者
https://doi.org/10.1007/978-1-137-57647-7ng offset to facilitate the training of gaze pathway and we add the channel attention module. We use Transformer to capture the relationship between the person and the predicted target in the heatmap pathway. Experimental results have demonstrated the effectiveness of our solution on GazeFollow dataset and DL Gaze dataset.
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