Extended DISFA Dataset: Investigating Posed and Spontaneous Facial Expressions

Mohammad Mavadati, Peyten Sanger, Mohammad H. Mahoor; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2016, pp. 1-8

Abstract


Automatic facial expression recognition (FER) is an important component of affect-aware technologies. Because of the lack of labeled spontaneous data, majority of existing automated FER systems were trained on posed facial expressions; however in real-world applications we deal with (subtle) spontaneous facial expression. This paper introduces an extension of DISFA, a previously released and well-accepted face dataset. Extended DISFA (DISFA+) has the following features: 1) it contains a large set of posed and spontaneous facial expressions data for a same group of individuals, 2) it provides the manually labeled frame-based annotations of 5-level intensity of twelve FACS facial actions, 3) it provides meta data (i.e. facial landmark points in addition to the self-report of each individual regarding every posed facial expression). This paper introduces and employs DISFA+, to analyze and compare temporal patterns and dynamic characteristics of posed and spontaneous facial expressions.

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[bibtex]
@InProceedings{Mavadati_2016_CVPR_Workshops,
author = {Mavadati, Mohammad and Sanger, Peyten and Mahoor, Mohammad H.},
title = {Extended DISFA Dataset: Investigating Posed and Spontaneous Facial Expressions},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2016}
}