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

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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




書目名稱Biometric Recognition年度引用




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




書目名稱Biometric Recognition讀者反饋




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

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Muhammad Hasan Amara,Abd Al-Rahman Mar’I performance is largely influenced by block generation and post-processing, concealing the performance of face classification core module. Secondly, implementing and optimizing all the three steps results in a very heavy work, which is a big barrier for researchers who only cares about classificatio

Paleontology

ALT

Muhammad Hasan Amara,Abd Al-Rahman Mar’Iand estimates 2D and 3D facial feature points simultaneously. In stage one, 2D and 3D facial feature points are roughly detected on the input face image, and head pose analysis is applied based on the 3D facial feature points to estimate its head pose. The face is then classified into one of three c

Recessive

Language Attitudes and Ideologies,to large variations due to differences in expressions and pose. Unlike previous shape regression-based approaches, we propose to reference features weighted by three different face landmarks, which are much more robust to shape variations. Then, a correlation-based feature selection method and a two

CLIFF

https://doi.org/10.1007/0-306-47588-Xrification, face detection and face alignment. However, face alignment remains a challenging problem due to large pose variation and the lack of data. Although researchers have designed various network architecture to handle this problem, pose information was rarely used explicitly. In this paper, w

Assault

https://doi.org/10.1007/0-306-47588-Xolutional Network (TCDCN), which are complicated and difficult to train. To solve this problem, this paper proposes a new Single Deep CNN (SDN). Unlike cascaded CNNs, SDN stacks three layer groups: each group consists of two convolutional layers and a max-pooling layer. This network structure can ex

enfeeble

錯誤

https://doi.org/10.1007/0-306-47588-Xo use different landmarks of faces to solve the problems caused by poses. In order to increase the ability of verification, semi-verification signal is used for training one network. The final face representation is formed by catenating features of two deep CNNs after PCA reduction. What’s more, eac