Transfer learning to account for idiosyncrasy in face and body expressions

Romera-Paredes, Bernardino, Aung, Min S. H., Pontil, Massimiliano, Bianchi-Berthouze, Nadia, Williams, Amanda C. de C. and Watson, Paul (2013) Transfer learning to account for idiosyncrasy in face and body expressions. In: UNSPECIFIED.

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Abstract

In this paper we investigate the use of the Transfer Learning (TL) framework to extract the commonalities across a set of subjects and also to learn the way each individual instantiates these commonalities to model idiosyncrasy. To implement this we apply three variants of Multi Task Learning, namely: Regularized Multi Task Learning (RMTL), Multi Task Feature Learning (MTFL) and Composite Multi Task Feature Learning (CMTFL). Two datasets are used; the first is a set of point based facial expressions with annotated discrete levels of pain. The second consists of full body motion capture data taken from subjects diagnosed with chronic lower back pain. A synchronized electromyographic signal from the lumbar paraspinal muscles is taken as a pain-related behavioural indicator. We compare our approaches with Ridge Regression which is a comparable model without the Transfer Learning property; as well as with a subtractive method for removing idiosyncrasy. The TL based methods show statistically significant improvements in correlation coefficients between predicted model outcomes and the target values compared to baseline models. In particular RMTL consistently outperforms all other methods; a paired t-test between RMTL and the best performing baseline method returned a maximum p-value of 2.3 × 10 -4 .

Item Type: Conference or Workshop Item (Paper)
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 26 Sep 2019 08:30
Last Modified: 22 Apr 2020 09:42
URI: https://ueaeprints.uea.ac.uk/id/eprint/72379
DOI: 10.1109/FG.2013.6553779

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