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

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Pose Aided Deep Convolutional Neural Networks for Face Alignmentrification, 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

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Face Landmark Localization Using a Single Deep Networkolutional 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

Mechanics

Cascaded Regression for 3D Face Alignmentted by face shape deformations and poor light conditions. With the assist of extra shape information provided by 3D facial model, these difficulties can be eased to some degree. In this paper, we propose 3D Cascaded Regression for detecting facial landmarks on 3D faces. Our algorithm makes full use

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Deep CNNs for Face Verificationo 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

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A Semi-supervised Learning Algorithm Based on Low Rank and Weighted Sparse Graph for Face Recognitio novel low rank and weighted sparse graph. First, we utilize exact low rank representation by the nuclear norm and Forbenius norm to capture the global subspace structure. Meanwhile, we build the weighted sparse regularization term with shape interaction information to capture the local linear struc

GENRE

Multilinear Local Fisher Discriminant Analysis for Face Recognitiontion. MLFDA achieves feature extraction by finding a multilinear projection to map the original tensor space into a tensor subspace that maximize the local between-class scatter as well as minimize the local within-class scatter. The experimental result shows that MLFDA has an outperformance.