@inproceedings{4af378b9e0f34fdeafde68c59bbe3aa6,
title = "Automatic facial expression recognition for affective computing based on bag of distances",
abstract = "In the recent years, the video-based approach is a popular choice for modeling and classifying facial expressions. However, this kind of methods require to segment different facial expressions prior to recognition, which might be a challenging task given real world videos. Thus, in this paper, we propose a novel facial expression recognition method based on extracting discriminative features from a still image. Our method first combines holistic and local distance-based features so that facial expressions could be characterized in more detail. The combined distance-based features are subsequently quantized to form mid-level features using the bag of words approach. The synergistic effect of these steps leads to much improved class separability and thus we can use a typical method, e.g., Support Vector Machine (SVM), to perform classification. We have performed the experiment on the Extended Cohn-Kanade (CK+) dataset. The experiment results show that the proposed scheme is efficient and accurate in facial expression recognition.",
keywords = "Affective Computing, bag of words, Facial expression recognition, facial features",
author = "Hsu, {Fu Song} and Lin, {Wei Yang} and Tsai, {Tzu Wei}",
year = "2013",
doi = "10.1109/APSIPA.2013.6694238",
language = "English",
isbn = "9789869000604",
series = "2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013",
booktitle = "2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2013",
note = "null ; Conference date: 29-10-2013 Through 01-11-2013",
}