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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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發(fā)表于 2025-3-21 17:56:02 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Biometric Recognition
期刊簡稱17th Chinese Confere
影響因子2023Wei Jia,Wenxiong Kang,Jun Wang
視頻videohttp://file.papertrans.cn/189/188172/188172.mp4
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Biometric Recognition; 17th Chinese Confere Wei Jia,Wenxiong Kang,Jun Wang Conference proceedings 2023 The Editor(s) (if applicable) and Th
影響因子.This book constitutes the proceedings of the 17th Chinese Conference, CCBR 2023, held in Xuzhou, China, during December 1–3, 2023...The 41 full papers included in this volume were carefully reviewed and selected from 79 submissions. The volume is divided in topical sections named: Fingerprint, Palmprint and Vein Recognition; Face Detection, Recognition and Tracking; Affective Computing and Human-Computer Interface; Trustworthy, Privacy and Personal Data Security; Medical and Other Applications.?.
Pindex Conference proceedings 2023
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書目名稱Biometric Recognition影響因子(影響力)




書目名稱Biometric Recognition影響因子(影響力)學(xué)科排名




書目名稱Biometric Recognition網(wǎng)絡(luò)公開度




書目名稱Biometric Recognition網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Biometric Recognition被引頻次




書目名稱Biometric Recognition被引頻次學(xué)科排名




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書目名稱Biometric Recognition年度引用學(xué)科排名




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書目名稱Biometric Recognition讀者反饋學(xué)科排名




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沙發(fā)
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MultiBioGM: A Hand Multimodal Biometric Model Combining Texture Prior Knowledge to?Enhance Generalizralization model MultiBioGM. Experimental results on three multimodal datasets demonstrate the effectiveness of our model for biometrics, which achieves 0.098%, 0.024%, and 0.117% EERs on unobserved data.
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Facial Adversarial Sample Augmentation for?Robust Low-Quality 3D Face Recognitionodule, a distribution alignment loss is designed to make the distribution of facial adversarial samples gradually close to the one of the original facial samples, and the common and valuable information from both distributions can be effectively extracted. Extensive experiments conducted on the CAS-
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More on Generalized Derivatives, palmprint images. Lastly, we reconstruct the super-resolution palmprint images with clear palmprint-specific texture and edge characteristics via two convolutional layers with embedding a PixelShuffle. Experimental results on three public palmprint databases clearly show the effectiveness of the pr
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