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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 Prediction of Online Psychological Help-Seeking Behavior During the COVID-19 Pandemic: An Interpretable Machine Learning Method Liu, Hui; Zhang, Lin; Wang, Weijun... In: Front Public Health (2022) BASE Show details 2 An Initial Investigation for Detecting Partially Spoofed Audio Zhang, Lin; Wang, Xin; Cooper, Erica... In: Proceedings of Interspeech 2021 ; Interspeech 2021 ; https://hal.archives-ouvertes.fr/hal-03555441 ; Interspeech 2021, Aug 2021, Brno, Czech Republic. pp.4264-4268, ⟨10.21437/Interspeech.2021-738⟩ (2021) BASE Show details 3 A Straightforward and Efficient Instance-Aware Curved Text Detector Zhao, Fan; Shao, Sidi; Zhang, Lin... In: Sensors (Basel) (2021) BASE Show details 4 The Dialectic Dimension of Ecological Cultural View in the Context of Marxist Theory ZHANG, Lin In: Cross-Cultural Communication; Vol 17, No 4 (2021): Cross-Cultural Communication; 16-21 ; 1923-6700 ; 1712-8358 (2021) BASE Show details 5 Language Testing as a Profession: An Interview with Yan Jin ... Zhang, Lin. - : Zenodo, 2020 BASE Show details 6 Language Testing as a Profession: An Interview with Yan Jin ... Zhang, Lin. - : Zenodo, 2020 BASE Show details 7 CytoGPS: A large-scale karyotype analysis of CML data Abrams, Zachary B.; Li, Suli; Zhang, Lin... In: Cancer Genet (2020) BASE Show details 8 3D Face Recognition Based on Multiple Keypoint Descriptors and Sparse Representation Zhang, Lin; Ding, Zhixuan; Li, Hongyu; Shen, Ying; Lu, Jianwei. - : Public Library of Science, 2014 Abstract: Recent years have witnessed a growing interest in developing methods for 3D face recognition. However, 3D scans often suffer from the problems of missing parts, large facial expressions, and occlusions. To be useful in real-world applications, a 3D face recognition approach should be able to handle these challenges. In this paper, we propose a novel general approach to deal with the 3D face recognition problem by making use of multiple keypoint descriptors (MKD) and the sparse representation-based classification (SRC). We call the proposed method 3DMKDSRC for short. Specifically, with 3DMKDSRC, each 3D face scan is represented as a set of descriptor vectors extracted from keypoints by meshSIFT. Descriptor vectors of gallery samples form the gallery dictionary. Given a probe 3D face scan, its descriptors are extracted at first and then its identity can be determined by using a multitask SRC. The proposed 3DMKDSRC approach does not require the pre-alignment between two face scans and is quite robust to the problems of missing data, occlusions and expressions. Its superiority over the other leading 3D face recognition schemes has been corroborated by extensive experiments conducted on three benchmark databases, Bosphorus, GavabDB, and FRGC2.0. The Matlab source code for 3DMKDSRC and the related evaluation results are publicly available at http://sse.tongji.edu.cn/linzhang/3dmkdsrcface/3dmkdsrc.htm. Keyword: Research Article URL: http://www.ncbi.nlm.nih.gov/pubmed/24940876 https://doi.org/10.1371/journal.pone.0100120 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4062431 BASE Hide details

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