Proceedings of the 25th ACM International Conference on Multimedia 2017
DOI: 10.1145/3123266.3123367
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Learning a Target Sample Re-Generator for Cross-Database Micro-Expression Recognition

Abstract: In this paper, we investigate the cross-database micro-expression recognition problem, where the training and testing samples are from two di erent micro-expression databases. Under this setting, the training and testing samples would have di erent feature distributions and hence the performance of most existing microexpression recognition methods may decrease greatly. To solve this problem, we propose a simple yet e ective method called Target Sample Re-Generator (TSRG) in this paper. By using TSRG, we are ab… Show more

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Cited by 32 publications
(44 citation statements)
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“…Recently, the problem of cross-domain microexpression recognition has gradually drawn the researchers' attentions. In the work of [17], Zong et al first investigated unsupervised cross-database microexpression recognition by proposing a target sample regenerator (TSRG) method. This method targets at learning a sample regenerator to regenerate the source and target microexpression samples such that the regenerated source and target samples would have the same or similar feature distributions.…”
Section: A Unsupervised Cross-domain Micro-expression Recognitionmentioning
confidence: 99%
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“…Recently, the problem of cross-domain microexpression recognition has gradually drawn the researchers' attentions. In the work of [17], Zong et al first investigated unsupervised cross-database microexpression recognition by proposing a target sample regenerator (TSRG) method. This method targets at learning a sample regenerator to regenerate the source and target microexpression samples such that the regenerated source and target samples would have the same or similar feature distributions.…”
Section: A Unsupervised Cross-domain Micro-expression Recognitionmentioning
confidence: 99%
“…Following the work of [17], the face images of the video clips from CASME II are cropped and transformed to 308 × 257 pixels, while for SMIC databases, we crop and then transform the images of microexpression samples into 170 × 139 pixels. As for performance evaluation metrics, we report the experimental results in terms of both weighted average recall (WAR) and unweighted average recall (UAR), which are widely used in the cross-domain speech emotion recognition research [19].…”
Section: A Experimental Protocolmentioning
confidence: 99%
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