Titlebook: Biometric Recognition; 12th Chinese Confere Jie Zhou,Yunhong Wang,Shiqi Yu Conference proceedings 2017 Springer International Publishing AG

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Three Dimensional Face Recognition via Surface Harmonic Mapping and Deep Learninggiven a 3D face scan, we first run the pre-processing pipeline and detect three main facial landmarks (i.e., nose tip and two inner eye corners). Then, harmonic mapping is employed to map the 3D coordinates and differential geometry quantities (e.g., normal vectors, curvatures) of each 3D face scan

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2D-3D Heterogeneous Face Recognition Based on Deep Canonical Correlation Analysisties, has become more important due to its scientific challenges and application potentials. In this paper, we propose a novel and effective approach, which adapts the Deep Canonical Correlation Analysis (Deep CCA) network to such an issue. Two solutions are presented to speed up the training proces

经典

Age Estimation by Refining Label Distribution in Deep CNNng period of our algorithm. The first one finds the optimal parameters of supervised deep CNN by given the label distribution of the training sample as the ground truth, while the second one estimates the variances of label distribution to fit the output of the CNN. These two tasks are performed alt

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Face Recognition via Heuristic Deep Active Learningow and expensive. An effective approach to reduce the annotation effort is active learning (AL). However, the traditional AL methods are limited by the hand-craft features and the small-scale datasets. In this paper, we propose a novel deep active learning framework combining the optimal feature rep

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One-Snapshot Face Anti-spoofing Using a Light Field Camerae, a reliable way to detect malicious attacks is crucial to the robustness of the face recognition system. This paper describes a new approach to utilizing light field camera for defending spoofing face attacks, like (warped) printed 2D facial photos and high-definition tablet images. The light fiel

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Matching Depth to RGB for Boosting Face Verificatione been proposed to improve the RGB-to-RGB face matcher by fusing it with the Depth-to-Depth face matcher. Yet, few efforts have been devoted to the matching between RGB and Depth face images. In this paper, we propose two deep convolutional neural network (DCNN) based approaches to Depth-to-RGB face

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indignant

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