Dynamic ensembles of exemplar-SVMs for still-to-video face recognition
作者：
Highlights：
• An efficient multi-classifier system is proposed for robust still-to-video FR.
• Multiple diverse representations are generated from the single target still face.
• Individual-specific ensembles of exemplar-SVM are designed based on domain adaptation.
• Different domain adaptation training schemes are proposed to generate the classifiers.
• Dynamic classifier selection and weighting are applied to perform spatio-temporal FR.
摘要
•An efficient multi-classifier system is proposed for robust still-to-video FR.•Multiple diverse representations are generated from the single target still face.•Individual-specific ensembles of exemplar-SVM are designed based on domain adaptation.•Different domain adaptation training schemes are proposed to generate the classifiers.•Dynamic classifier selection and weighting are applied to perform spatio-temporal FR.
论文关键词：Video surveillance,Watch-list screening,Face recognition,Single sample per person,Multi-classifier system,Random subspace methods,Domain adaptation,Dynamic classifier selection
论文评审过程：Received 28 September 2016, Revised 12 February 2017, Accepted 12 April 2017, Available online 13 April 2017, Version of Record 19 April 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.04.014
