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

debouch

Combining Multiple Features for Cross-Domain Face Sketch Recognitionan intra-modality method called the Eigentransformation and two inter-modality methods based on modality invariant features, namely the Multiscale Local Binary Pattern (MLBP) and the Histogram of Averaged Orientation Gradients (HAOG). Meanwhile, a sum-score fusion of min-max normalized scores is app

幾何學(xué)家

browbeat

Exploring Deep Features with Different Distance Measures for Still to Video Face Matchinge often captured with high quality and cooperative user condition. On the contrary, video clips usually show more variations and of low quality. In this paper, we primarily focus on the S2V face recognition where face gallery is formed by a few still face images, and the query is the video clip. We

GROSS

0302-9743 BR 2016, held in Chengdu, China, in October 2016...The 84 revised full papers presented in this book were carefully reviewed and selected from 138 submissions. The papers focus on Face Recognition and Analysis; Fingerprint, Palm-print and Vascular Biometrics; Iris and Ocular Biometrics; Behavioral B

bizarre

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Compact Face Representation via Forward Model Selectionl selection algorithm is designed to simultaneously select the complementary face models and generate the compact representation. Employing a public dataset as training set and fusing by only six selected face networks, the recognition system with this compact face representation achieves 99.05?% accuracy on LFW benchmark.

設(shè)施

Oligarchy

https://doi.org/10.1007/0-306-47588-X 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.

Yag-Capsulotomy

Language Attitudes and Ideologies,-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.

Celiac-Plexus

https://doi.org/10.1007/0-306-47588-Xtract more global high-level features, which express the face landmarks more precisely. Extensive experiments show that SDN outperforms existing DCNN methods and is robust to large pose variation, lighting and even severe occlusion. While the network complexity is also reduced obviously compared to other methods.