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Titlebook: Biometric Recognition; 9th Chinese Conferen Zhenan Sun,Shiguang Shan,Weiqi Yuan Conference proceedings 2014 Springer International Publishi

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樓主: Callow
11#
發(fā)表于 2025-3-23 10:23:22 | 只看該作者
12#
發(fā)表于 2025-3-23 14:52:45 | 只看該作者
13#
發(fā)表于 2025-3-23 18:27:40 | 只看該作者
David Lasagabaster,Aintzane Doizingle task feature selection, the proposed algorithm can get higher recognition rate through sharing the related information among tasks. At the same time, the stability analysis is introduced to feature selection. With given stability metrics, the results of experiments show that features selected with the proposed algorithm are more stable.
14#
發(fā)表于 2025-3-23 23:40:21 | 只看該作者
Language Planning and Microlinguistics ASIFT dimension using principal component analysis and get PCA-ASIFT. The PCA-ASIFT’s discriminative capability drops because of the dimension reduction. It brings about more false SIFT matching. We further introduce the SSIM to reduce the false matching. The experimental results show the efficiency of the proposed approach.
15#
發(fā)表于 2025-3-24 04:35:09 | 只看該作者
https://doi.org/10.1057/9781137361240. Then the Fourier statistics and LBP are combined together to form a new feature vector. Finally, a SVM classifier is trained to discriminate the live and forge face image. The experimental results on the NUAA demonstrate that the proposed scheme is efficient and robust.
16#
發(fā)表于 2025-3-24 08:32:33 | 只看該作者
17#
發(fā)表于 2025-3-24 14:24:23 | 只看該作者
Multi-Task Learning for Face Ethnicity and Gender Recognitioningle task feature selection, the proposed algorithm can get higher recognition rate through sharing the related information among tasks. At the same time, the stability analysis is introduced to feature selection. With given stability metrics, the results of experiments show that features selected with the proposed algorithm are more stable.
18#
發(fā)表于 2025-3-24 17:19:28 | 只看該作者
19#
發(fā)表于 2025-3-24 19:16:32 | 只看該作者
20#
發(fā)表于 2025-3-25 01:59:34 | 只看該作者
Jan Wind,Brunetto Chiarelli,Abraham Jonkeres in the obtained skin point cloud, which mainly correspond to eyes, mouth, moustache, etc. We propose an approach based on nearest neighbor search method to fulfill the holes. Experiments show that the proposed approach can extract pure faces with different sizes, poses and expressions under various illumination conditions.
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