TY - GEN
T1 - One-class selective transfer machine for personalized anomalous facial expression detection
AU - Fujita, Hirofumi
AU - Matsukawa, Tetsu
AU - Suzuki, Einoshin
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85047784932&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85047784932&partnerID=8YFLogxK
U2 - 10.5220/0006613502740283
DO - 10.5220/0006613502740283
M3 - Conference contribution
AN - SCOPUS:85047784932
T3 - VISIGRAPP 2018 - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
SP - 274
EP - 283
BT - VISAPP
A2 - Imai, Francisco
A2 - Tremeau, Alain
A2 - Braz, Jose
PB - SciTePress
T2 - 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2018
Y2 - 27 January 2018 through 29 January 2018
ER -