Applying Grammar Inference To Identify Generalized Patterns of Facial Expressions of Pain

Ute Schmid, Michael Siebers, Dominik Seuß, Miriam Kunz, Stefan Lautenbacher
Proceedings of the Eleventh International Conference on Grammatical Inference, PMLR 21:183-188, 2012.

Abstract

We present an application of grammar inference in the domain of facial expression analysis. Typically, only sets of AUs which occur in a given time frame are used for expression analysis, the sequence in which these AUs occur is ignored. We wanted to explore whether the strucural patterns of AU appearances contain diagnostically relevant information. We applied alignment-based learning (ABL) to data of facial expressions of pain collected in a psychological study. To evaluate the quality of the induced grammars we applied cross-validation to estimate the generalization error. We can show that the learned grammars have reasonably high coverages for unseen pain sequences. However, the number of rules of the learned grammars cannot be reduced substantially without loss of generalization.

Cite this Paper

BibTeX
@InProceedings{pmlr-v21-schmid12a, title = {Applying Grammar Inference To Identify Generalized Patterns of Facial Expressions of Pain}, author = {Schmid, Ute and Siebers, Michael and Seuß, Dominik and Kunz, Miriam and Lautenbacher, Stefan}, booktitle = {Proceedings of the Eleventh International Conference on Grammatical Inference}, pages = {183--188}, year = {2012}, editor = {Heinz, Jeffrey and Higuera, Colin and Oates, Tim}, volume = {21}, series = {Proceedings of Machine Learning Research}, address = {University of Maryland, College Park, MD, USA}, month = {05--08 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v21/schmid12a/schmid12a.pdf}, url = {https://proceedings.mlr.press/v21/schmid12a.html}, abstract = {We present an application of grammar inference in the domain of facial expression analysis. Typically, only sets of AUs which occur in a given time frame are used for expression analysis, the sequence in which these AUs occur is ignored. We wanted to explore whether the strucural patterns of AU appearances contain diagnostically relevant information. We applied alignment-based learning (ABL) to data of facial expressions of pain collected in a psychological study. To evaluate the quality of the induced grammars we applied cross-validation to estimate the generalization error. We can show that the learned grammars have reasonably high coverages for unseen pain sequences. However, the number of rules of the learned grammars cannot be reduced substantially without loss of generalization.} }
Endnote
%0 Conference Paper %T Applying Grammar Inference To Identify Generalized Patterns of Facial Expressions of Pain %A Ute Schmid %A Michael Siebers %A Dominik Seuß %A Miriam Kunz %A Stefan Lautenbacher %B Proceedings of the Eleventh International Conference on Grammatical Inference %C Proceedings of Machine Learning Research %D 2012 %E Jeffrey Heinz %E Colin Higuera %E Tim Oates %F pmlr-v21-schmid12a %I PMLR %P 183--188 %U https://proceedings.mlr.press/v21/schmid12a.html %V 21 %X We present an application of grammar inference in the domain of facial expression analysis. Typically, only sets of AUs which occur in a given time frame are used for expression analysis, the sequence in which these AUs occur is ignored. We wanted to explore whether the strucural patterns of AU appearances contain diagnostically relevant information. We applied alignment-based learning (ABL) to data of facial expressions of pain collected in a psychological study. To evaluate the quality of the induced grammars we applied cross-validation to estimate the generalization error. We can show that the learned grammars have reasonably high coverages for unseen pain sequences. However, the number of rules of the learned grammars cannot be reduced substantially without loss of generalization.
RIS
TY - CPAPER TI - Applying Grammar Inference To Identify Generalized Patterns of Facial Expressions of Pain AU - Ute Schmid AU - Michael Siebers AU - Dominik Seuß AU - Miriam Kunz AU - Stefan Lautenbacher BT - Proceedings of the Eleventh International Conference on Grammatical Inference DA - 2012/08/16 ED - Jeffrey Heinz ED - Colin Higuera ED - Tim Oates ID - pmlr-v21-schmid12a PB - PMLR DP - Proceedings of Machine Learning Research VL - 21 SP - 183 EP - 188 L1 - http://proceedings.mlr.press/v21/schmid12a/schmid12a.pdf UR - https://proceedings.mlr.press/v21/schmid12a.html AB - We present an application of grammar inference in the domain of facial expression analysis. Typically, only sets of AUs which occur in a given time frame are used for expression analysis, the sequence in which these AUs occur is ignored. We wanted to explore whether the strucural patterns of AU appearances contain diagnostically relevant information. We applied alignment-based learning (ABL) to data of facial expressions of pain collected in a psychological study. To evaluate the quality of the induced grammars we applied cross-validation to estimate the generalization error. We can show that the learned grammars have reasonably high coverages for unseen pain sequences. However, the number of rules of the learned grammars cannot be reduced substantially without loss of generalization. ER -
APA
Schmid, U., Siebers, M., Seuß, D., Kunz, M. & Lautenbacher, S.. (2012). Applying Grammar Inference To Identify Generalized Patterns of Facial Expressions of Pain. Proceedings of the Eleventh International Conference on Grammatical Inference, in Proceedings of Machine Learning Research 21:183-188 Available from https://proceedings.mlr.press/v21/schmid12a.html.

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