One-class selective transfer machine for personalized anomalous facial expression detection

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

An anomalous facial expression is a facial expression which scarcely occurs in daily life and coveys cues about an anomalous physical or mental condition. In this paper, we propose a one-class transfer learning method for detecting the anomalous facial expressions. In facial expression detection, most articles propose generic models which predict the classes of the samples for all persons. However, people vary in facial morphology, e.g., thick versus thin eyebrows, and such individual differences often cause prediction errors. While a possible solution would be to learn a single-task classifier from samples of the target person only, it will often overfit due to the small sample size of the target person in real applications. To handle individual differences in anomaly detection, we extend Selective Transfer Machine (STM) (Chu et al., 2013), which learns a personalized multi-class classifier by re-weighting samples based on their proximity to the target samples. In contrast to related methods for personalized models on facial expressions, including STM, our method learns a one-class classifier which requires only one-class target and source samples, i.e., normal samples, and thus there is no need to collect anomalous samples which scarcely occur. Experiments on a public dataset show that our method outperforms generic and single-task models using one-class SVM, and a state-of-the-art multi-task learning method.

Original languageEnglish
Title of host publicationVISAPP
EditorsJose Braz, Francisco Imai, Alain Tremeau
PublisherSciTePress
Pages274-283
Number of pages10
ISBN (Electronic)9789897582905
Publication statusPublished - Jan 1 2018
Event13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2018 - Funchal, Madeira, Portugal
Duration: Jan 27 2018Jan 29 2018

Publication series

NameVISIGRAPP 2018 - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
Volume5

Other

Other13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2018
CountryPortugal
CityFunchal, Madeira
Period1/27/181/29/18

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All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design
  • Artificial Intelligence

Cite this

Fujita, H., Matsukawa, T., & Suzuki, E. (2018). One-class selective transfer machine for personalized anomalous facial expression detection. In J. Braz, F. Imai, & A. Tremeau (Eds.), VISAPP (pp. 274-283). (VISIGRAPP 2018 - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications; Vol. 5). SciTePress.