@inproceedings{27c5482dcb804934a7dc40a22d7c3f0c,
title = "Why is facial expression analysis in the wild challenging?",
abstract = "In this paper, we discuss the challenges for facial expression analysis in the wild. We studied the problems exemplarily on the Emotion Recognition in the Wild Challenge 2013 [3] dataset. We performed extensive experiments on this dataset comparing different approaches for face alignment, face representation, and classification, as well as human performance. It turns out that under close-to-real conditions, especially with co-occurring speech, it is hard even for humans to assign emotion labels to clips when only taking video into account. Our experiments on automatic emotion classification achieved at best a correct classification rate of 29.81% on the test set using Gabor features and linear support vector machines, which were trained on web images. This result is 7.06% better than the official baseline, which additionally incorporates time information.",
keywords = "DCT, Emotion, EmotiW, Facial expression, FACS, Gabor, LBP, SVM",
author = "Tobias Gehrig and Ekenel, {Hazim Kemal}",
year = "2013",
doi = "10.1145/2531923.2531924",
language = "English",
isbn = "9781450325646",
series = "EmotiW 2013 - Proceedings of the 2013 ACM Emotion Recognition in the Wild Challenge and Workshop, Co-located with ICMI 2013",
publisher = "Association for Computing Machinery",
pages = "9--16",
booktitle = "EmotiW 2013 - Proceedings of the 2013 ACM Emotion Recognition in the Wild Challenge and Workshop, Co-located with ICMI 2013",
note = "2013 1st ACM Emotion Recognition in the Wild Challenge and Workshop, EmotiW 2013 - Co-located with ICMI 2013 ; Conference date: 09-12-2013 Through 09-12-2013",
}