Ensemble Learning Based on Convolutional Kernel Networks Features for Kinship Verification

Qiang Guo, Bo Ma, Tianming Lan

科研成果: 书/报告/会议事项章节会议稿件同行评审

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摘要

Kinship verification based on facial images is one of the popular research topic in the field of face recognition. It's still a challenging problem due to many inevitable factors, such as varying illumination, poses, and expressions. And traditional handcrafted features are usually not robust enough. For the above reasons, in this paper, we extract kernel features by Convolutional Kernel Networks (CKN), which are invariant to particular transformations. After extracting the CKN features, we use feature bagging to classify. It's an ensemble learning method that attempts to reduce the correlation between estimators in an ensemble by training them on random samples of features instead of the entire feature set. Specifically, the CKN features are randomly sampled to train an SVM classifier each time, and then multiple SVM classifiers are combined by majority voting to make prediction. In addition, we collect a large kinship face dataset named LarG-KinFace from Internet search under uncontrolled conditions. The proposed method is evaluated on three datasets KinFaceW-I, KinFaceW-II, and LarG-KinFace. Experimental results demonstrate the efficacy of the proposed method.

源语言英语
主期刊名2018 IEEE International Conference on Multimedia and Expo, ICME 2018
出版商IEEE Computer Society
ISBN(电子版)9781538617373
DOI
出版状态已出版 - 8 10月 2018
活动2018 IEEE International Conference on Multimedia and Expo, ICME 2018 - San Diego, 美国
期限: 23 7月 201827 7月 2018

出版系列

姓名Proceedings - IEEE International Conference on Multimedia and Expo
2018-July
ISSN(印刷版)1945-7871
ISSN(电子版)1945-788X

会议

会议2018 IEEE International Conference on Multimedia and Expo, ICME 2018
国家/地区美国
San Diego
时期23/07/1827/07/18

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引用此

Guo, Q., Ma, B., & Lan, T. (2018). Ensemble Learning Based on Convolutional Kernel Networks Features for Kinship Verification. 在 2018 IEEE International Conference on Multimedia and Expo, ICME 2018 文章 8486585 (Proceedings - IEEE International Conference on Multimedia and Expo; 卷 2018-July). IEEE Computer Society. https://doi.org/10.1109/ICME.2018.8486585