Tampere University of Technology

TUTCRIS Research Portal

Facial expression classification based on local spatiotemporal edge and texture descriptors

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Standard

Facial expression classification based on local spatiotemporal edge and texture descriptors. / Gizatdinova, Yulia; Surakka, Veikko; Zhao, Guoying; Mäkinen, Erno; Raisamo, Roope.

Selected Papers from the Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research - Digital Edition, MB'10. 2011. 21.

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Gizatdinova, Y, Surakka, V, Zhao, G, Mäkinen, E & Raisamo, R 2011, Facial expression classification based on local spatiotemporal edge and texture descriptors. in Selected Papers from the Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research - Digital Edition, MB'10., 21, 7th International Conference on Methods and Techniques in Behavioral Research, MB'10, Eindhoven, Netherlands, 24/08/10. https://doi.org/10.1145/1931344.1931365

APA

Gizatdinova, Y., Surakka, V., Zhao, G., Mäkinen, E., & Raisamo, R. (2011). Facial expression classification based on local spatiotemporal edge and texture descriptors. In Selected Papers from the Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research - Digital Edition, MB'10 [21] https://doi.org/10.1145/1931344.1931365

Vancouver

Gizatdinova Y, Surakka V, Zhao G, Mäkinen E, Raisamo R. Facial expression classification based on local spatiotemporal edge and texture descriptors. In Selected Papers from the Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research - Digital Edition, MB'10. 2011. 21 https://doi.org/10.1145/1931344.1931365

Author

Gizatdinova, Yulia ; Surakka, Veikko ; Zhao, Guoying ; Mäkinen, Erno ; Raisamo, Roope. / Facial expression classification based on local spatiotemporal edge and texture descriptors. Selected Papers from the Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research - Digital Edition, MB'10. 2011.

Bibtex - Download

@inproceedings{8199214ce91f40889cc75bd83a97be17,
title = "Facial expression classification based on local spatiotemporal edge and texture descriptors",
abstract = "Facial expressions are emotionally, socially and otherwise meaningful reflective signals in the face. Facial expressions play a critical role in human life, providing an important channel of nonverbal communication. Automation of the entire process of expression analysis can potentially facilitate human-computer interaction, making it to resemble mechanisms of human-human communication. In this paper, we present an ongoing research that aims at development of a novel spatiotemporal approach to expression classification in video. The novelty comes from a new facial representation that is based on local spatiotemporal feature descriptors. In particular, a combined dynamic edge and texture information is used for reliable description of both appearance and motion of the expression. Support vector machines are utilized to perform a final expression classification. The planned experiments will further systematically evaluate the performance of the developed method with several databases of complex facial expressions.",
keywords = "Action unit, Emotion, Expression classification, Facial expression, Human behaviour understanding, Local binary pattern, Local oriented edge, Spatiotemporal descriptor",
author = "Yulia Gizatdinova and Veikko Surakka and Guoying Zhao and Erno M{\"a}kinen and Roope Raisamo",
year = "2011",
doi = "10.1145/1931344.1931365",
language = "English",
isbn = "9781605589268",
booktitle = "Selected Papers from the Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research - Digital Edition, MB'10",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Facial expression classification based on local spatiotemporal edge and texture descriptors

AU - Gizatdinova, Yulia

AU - Surakka, Veikko

AU - Zhao, Guoying

AU - Mäkinen, Erno

AU - Raisamo, Roope

PY - 2011

Y1 - 2011

N2 - Facial expressions are emotionally, socially and otherwise meaningful reflective signals in the face. Facial expressions play a critical role in human life, providing an important channel of nonverbal communication. Automation of the entire process of expression analysis can potentially facilitate human-computer interaction, making it to resemble mechanisms of human-human communication. In this paper, we present an ongoing research that aims at development of a novel spatiotemporal approach to expression classification in video. The novelty comes from a new facial representation that is based on local spatiotemporal feature descriptors. In particular, a combined dynamic edge and texture information is used for reliable description of both appearance and motion of the expression. Support vector machines are utilized to perform a final expression classification. The planned experiments will further systematically evaluate the performance of the developed method with several databases of complex facial expressions.

AB - Facial expressions are emotionally, socially and otherwise meaningful reflective signals in the face. Facial expressions play a critical role in human life, providing an important channel of nonverbal communication. Automation of the entire process of expression analysis can potentially facilitate human-computer interaction, making it to resemble mechanisms of human-human communication. In this paper, we present an ongoing research that aims at development of a novel spatiotemporal approach to expression classification in video. The novelty comes from a new facial representation that is based on local spatiotemporal feature descriptors. In particular, a combined dynamic edge and texture information is used for reliable description of both appearance and motion of the expression. Support vector machines are utilized to perform a final expression classification. The planned experiments will further systematically evaluate the performance of the developed method with several databases of complex facial expressions.

KW - Action unit

KW - Emotion

KW - Expression classification

KW - Facial expression

KW - Human behaviour understanding

KW - Local binary pattern

KW - Local oriented edge

KW - Spatiotemporal descriptor

UR - http://www.scopus.com/inward/record.url?scp=79952499491&partnerID=8YFLogxK

U2 - 10.1145/1931344.1931365

DO - 10.1145/1931344.1931365

M3 - Conference contribution

SN - 9781605589268

BT - Selected Papers from the Proceedings of the 7th International Conference on Methods and Techniques in Behavioral Research - Digital Edition, MB'10

ER -