antipsychotic
发表于 2025-3-25 04:00:45
An Overview and Forecast of Biometric Recognition Technology Used in Forensic Sciencependent 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.
Efflorescent
发表于 2025-3-25 08:36:24
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步履蹒跚
发表于 2025-3-25 14:26:13
Estimation of Gaze-Following Based on Transformer and the Guiding Offsetng 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.
adduction
发表于 2025-3-25 17:38:18
https://doi.org/10.1057/9780230502666 only has global and local correlation to achieve accurate extraction of veins, but also enables the model to maintain its lightweight characteristics. Our approach achieves good results on the public finger vein dataset SDU-FV, MMCBNU_6000.
CUMB
发表于 2025-3-25 22:34:34
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灾祸
发表于 2025-3-26 03:28:50
A Lightweight Segmentation Network Based on Extraction only has global and local correlation to achieve accurate extraction of veins, but also enables the model to maintain its lightweight characteristics. Our approach achieves good results on the public finger vein dataset SDU-FV, MMCBNU_6000.
assent
发表于 2025-3-26 04:56:00
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Infraction
发表于 2025-3-26 10:12:32
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anesthesia
发表于 2025-3-26 14:46:11
Conclusion: Community and Transcendencedifferent types of adversarial faces. Experimental results over adversarial examples and face forgery attacks show that the proposed detection method is effective with better generalizability and more adversarially robust comparing with previous methods.
BRIEF
发表于 2025-3-26 19:04:01
Disentanglement of Deep Features for Adversarial Face Detectiondifferent types of adversarial faces. Experimental results over adversarial examples and face forgery attacks show that the proposed detection method is effective with better generalizability and more adversarially robust comparing with previous methods.