Facial expression recognition by analyzing features of conceptual regions

Huiquan Zhang, Sha Luo, Osamu Yoshie

研究成果: Conference contribution

4 引用 (Scopus)

抄録

Facial expression recognition utilizes collection of information from characteristic actions to analyze emotions and mental states of a person. It has emerged as the pivotal research topics in areas such as human computer interaction, sentimental analysis and synthetic face animation over the last years. This paper proposes an approach for facial expression by discovering associations between visual feature and Local Binary Pattern (LBP). Unlike many previous studies, the proposed approach automatically tracks the facial area and segments face into meaningful areas based on description of Local Binary Pattern. And then it accumulates the probabilities throughout the frames from video data to capture the temporal characteristics of facial expressions by analyzing facial expressions. Through the proposed approach, the temporal variation of facial expression can be quantified in individual areas. Thus, the recognition process of facial expression tends to be more comprehensible without sacrificing results of recognition. The empirical evaluation results of the approach are realized using video data which is collected from 10 volunteers. The results demonstrated that the proposed approach can effectively segment face into specific area and recognize facial expression.

元の言語English
ホスト出版物のタイトル2013 IEEE/ACIS 12th International Conference on Computer and Information Science, ICIS 2013 - Proceedings
ページ529-534
ページ数6
DOI
出版物ステータスPublished - 2013
イベント2013 IEEE/ACIS 12th International Conference on Computer and Information Science, ICIS 2013 - Niigata
継続期間: 2013 6 162013 6 20

Other

Other2013 IEEE/ACIS 12th International Conference on Computer and Information Science, ICIS 2013
Niigata
期間13/6/1613/6/20

Fingerprint

Human computer interaction
Animation

ASJC Scopus subject areas

  • Computer Science (miscellaneous)
  • Information Systems

これを引用

Zhang, H., Luo, S., & Yoshie, O. (2013). Facial expression recognition by analyzing features of conceptual regions. : 2013 IEEE/ACIS 12th International Conference on Computer and Information Science, ICIS 2013 - Proceedings (pp. 529-534). [6607893] https://doi.org/10.1109/ICIS.2013.6607893

Facial expression recognition by analyzing features of conceptual regions. / Zhang, Huiquan; Luo, Sha; Yoshie, Osamu.

2013 IEEE/ACIS 12th International Conference on Computer and Information Science, ICIS 2013 - Proceedings. 2013. p. 529-534 6607893.

研究成果: Conference contribution

Zhang, H, Luo, S & Yoshie, O 2013, Facial expression recognition by analyzing features of conceptual regions. : 2013 IEEE/ACIS 12th International Conference on Computer and Information Science, ICIS 2013 - Proceedings., 6607893, pp. 529-534, 2013 IEEE/ACIS 12th International Conference on Computer and Information Science, ICIS 2013, Niigata, 13/6/16. https://doi.org/10.1109/ICIS.2013.6607893
Zhang H, Luo S, Yoshie O. Facial expression recognition by analyzing features of conceptual regions. : 2013 IEEE/ACIS 12th International Conference on Computer and Information Science, ICIS 2013 - Proceedings. 2013. p. 529-534. 6607893 https://doi.org/10.1109/ICIS.2013.6607893
Zhang, Huiquan ; Luo, Sha ; Yoshie, Osamu. / Facial expression recognition by analyzing features of conceptual regions. 2013 IEEE/ACIS 12th International Conference on Computer and Information Science, ICIS 2013 - Proceedings. 2013. pp. 529-534
@inproceedings{a777ad9a973d4912aa7c2497b7d8aa94,
title = "Facial expression recognition by analyzing features of conceptual regions",
abstract = "Facial expression recognition utilizes collection of information from characteristic actions to analyze emotions and mental states of a person. It has emerged as the pivotal research topics in areas such as human computer interaction, sentimental analysis and synthetic face animation over the last years. This paper proposes an approach for facial expression by discovering associations between visual feature and Local Binary Pattern (LBP). Unlike many previous studies, the proposed approach automatically tracks the facial area and segments face into meaningful areas based on description of Local Binary Pattern. And then it accumulates the probabilities throughout the frames from video data to capture the temporal characteristics of facial expressions by analyzing facial expressions. Through the proposed approach, the temporal variation of facial expression can be quantified in individual areas. Thus, the recognition process of facial expression tends to be more comprehensible without sacrificing results of recognition. The empirical evaluation results of the approach are realized using video data which is collected from 10 volunteers. The results demonstrated that the proposed approach can effectively segment face into specific area and recognize facial expression.",
keywords = "conceptual regions, Facial expression recognition, image segmentation, local binary pattern",
author = "Huiquan Zhang and Sha Luo and Osamu Yoshie",
year = "2013",
doi = "10.1109/ICIS.2013.6607893",
language = "English",
isbn = "9781479901746",
pages = "529--534",
booktitle = "2013 IEEE/ACIS 12th International Conference on Computer and Information Science, ICIS 2013 - Proceedings",

}

TY - GEN

T1 - Facial expression recognition by analyzing features of conceptual regions

AU - Zhang, Huiquan

AU - Luo, Sha

AU - Yoshie, Osamu

PY - 2013

Y1 - 2013

N2 - Facial expression recognition utilizes collection of information from characteristic actions to analyze emotions and mental states of a person. It has emerged as the pivotal research topics in areas such as human computer interaction, sentimental analysis and synthetic face animation over the last years. This paper proposes an approach for facial expression by discovering associations between visual feature and Local Binary Pattern (LBP). Unlike many previous studies, the proposed approach automatically tracks the facial area and segments face into meaningful areas based on description of Local Binary Pattern. And then it accumulates the probabilities throughout the frames from video data to capture the temporal characteristics of facial expressions by analyzing facial expressions. Through the proposed approach, the temporal variation of facial expression can be quantified in individual areas. Thus, the recognition process of facial expression tends to be more comprehensible without sacrificing results of recognition. The empirical evaluation results of the approach are realized using video data which is collected from 10 volunteers. The results demonstrated that the proposed approach can effectively segment face into specific area and recognize facial expression.

AB - Facial expression recognition utilizes collection of information from characteristic actions to analyze emotions and mental states of a person. It has emerged as the pivotal research topics in areas such as human computer interaction, sentimental analysis and synthetic face animation over the last years. This paper proposes an approach for facial expression by discovering associations between visual feature and Local Binary Pattern (LBP). Unlike many previous studies, the proposed approach automatically tracks the facial area and segments face into meaningful areas based on description of Local Binary Pattern. And then it accumulates the probabilities throughout the frames from video data to capture the temporal characteristics of facial expressions by analyzing facial expressions. Through the proposed approach, the temporal variation of facial expression can be quantified in individual areas. Thus, the recognition process of facial expression tends to be more comprehensible without sacrificing results of recognition. The empirical evaluation results of the approach are realized using video data which is collected from 10 volunteers. The results demonstrated that the proposed approach can effectively segment face into specific area and recognize facial expression.

KW - conceptual regions

KW - Facial expression recognition

KW - image segmentation

KW - local binary pattern

UR - http://www.scopus.com/inward/record.url?scp=84886518443&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84886518443&partnerID=8YFLogxK

U2 - 10.1109/ICIS.2013.6607893

DO - 10.1109/ICIS.2013.6607893

M3 - Conference contribution

AN - SCOPUS:84886518443

SN - 9781479901746

SP - 529

EP - 534

BT - 2013 IEEE/ACIS 12th International Conference on Computer and Information Science, ICIS 2013 - Proceedings

ER -