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Titlebook: Biometric Recognition; 11th Chinese Confere Zhisheng You,Jie Zhou,Qijun Zhao Conference proceedings 2016 Springer International Publishing

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樓主: osteomalacia
51#
發(fā)表于 2025-3-30 09:21:41 | 只看該作者
52#
發(fā)表于 2025-3-30 13:33:49 | 只看該作者
Binary Classifiers and Radial Symmetry Transform for Fast and Accurate Eye Localization is employed to identify the real eyes from the reliable eye candidates. A large number of tests have been completed to verify the performance of the proposed algorithm. Experimental results demonstrate that the algorithm proposed in this article is robust and efficient.
53#
發(fā)表于 2025-3-30 16:49:06 | 只看該作者
Extended Robust Cascaded Pose Regression for Face Alignment-level boosted regression are applied to establish accurate relation between features and shapes. Experiments on two challenging face datasets (LFPW, COFW) show that our proposed approach significantly outperforms the state-of-art in terms of both efficiency and accuracy.
54#
發(fā)表于 2025-3-30 23:46:31 | 只看該作者
55#
發(fā)表于 2025-3-31 02:01:52 | 只看該作者
56#
發(fā)表于 2025-3-31 05:55:10 | 只看該作者
0302-9743 iometrics; Affective Computing; Feature Extraction and Classification Theory; Anti-Spoofing and Privacy; Surveillance; and DNA and Emerging Biometrics..978-3-319-46653-8978-3-319-46654-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
57#
發(fā)表于 2025-3-31 11:18:20 | 只看該作者
Muhammad Hasan Amara,Abd Al-Rahman Mar’Imprehensive set of candidate regions. In the latter, we further decide whether the regions are faces using a well-trained Faster R-CNN. Experiments are conducted on the WIDER FACE benchmark, and the results clearly prove the competency of the proposed method at detecting occluded faces.
58#
發(fā)表于 2025-3-31 16:32:34 | 只看該作者
59#
發(fā)表于 2025-3-31 19:15:46 | 只看該作者
60#
發(fā)表于 2025-4-1 00:57:32 | 只看該作者
https://doi.org/10.1007/978-3-030-74958-3cted by an effective post-processing method. We evaluate the proposed method by performing semi-supervised classification experiments on ORL, Extended Yale B and AR face database. The experimental results show that our approach improves the accuracy of semi-supervised learning and achieves the state-of-the-art performance.
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