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

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书目名称Biometric Recognition影响因子(影响力)




书目名称Biometric Recognition影响因子(影响力)学科排名




书目名称Biometric Recognition网络公开度




书目名称Biometric Recognition网络公开度学科排名




书目名称Biometric Recognition被引频次




书目名称Biometric Recognition被引频次学科排名




书目名称Biometric Recognition年度引用




书目名称Biometric Recognition年度引用学科排名




书目名称Biometric Recognition读者反馈




书目名称Biometric Recognition读者反馈学科排名

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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

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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

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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