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Cited by 9Emotion and Mood RecognitionFace recognition and analysisFace and Expression Recognition

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Spontaneous Facial Expression Recognition Based on Feature Point Tracking

He, Shan, Wang, Shangfei, Lv, Yanpeng·2011·2011 Sixth International Conference on Image and Graphics
DOI 10.1109/icig.2011.91
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Abstract

In recent years, facial expression recognition has attracted a lot of attention because of its importance in human-computer interaction. However, most previous work has focus on posed expression. In this paper, we propose a spontaneous facial expression recognition method based on feature point tracking. First, all expression sequences are normalized according to their pupils' coordinates. Second, 23 points are labeled manually in the onset and apex frames. Then Kalman filter is used for tracking. Two kinds of features, point displacement features and points distance variation features, are extracted. Finally, Hidden Markov Model is employed as classifier. Experiments have been conducted on the spontaneous facial expression database of USTC-NVIE. The results indicate that Kalman filter point tracking method could detect the right place of points, and the points distance variation features are more suitable than the point displacement features for the facial expression recognition.

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  • Previous FER methods treat the texture or landmark modality independently, using only one modality for FER.

    “Mostly, all previous work treats the texture or landmark modality independently, where only the texture or landmark modality is employed for FER.”

    From Multimodal learning for facial expression recognition (2015) · cites this paper as [31]

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Research works printed but not linked: 10

  1. 10.1093/acprof:oso/9780195179644.001.0001 · DOI
  2. 10.5772/4847 · DOI · Paper page
  3. 10.1016/j.ijhcs.2007.02.003 · DOI · Paper page
  4. 10.1109/TMM.2010.2060716 · DOI · Paper page
  5. 10.1109/MASSP.1986.1165342 · DOI · Paper page
  6. 10.1109/TPAMI.2008.52 · DOI · Paper page
  7. 10.1016/S0031-3203(02)00052-3 · DOI · Paper page
  8. 10.1109/ICIAS.2007.4658455 · DOI
  9. lv, 0, A spontaneous facial expression recognition method using head motion and aam features, Nature and Biologically Inspired Computing (NaBIC) 2010 Second World Congress on, 334
  10. 10.1145/1322192.1322198 · DOI

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Metadata

Authors
He, S., Wang, S., & Lv, Y.
Affiliations
University of Science and Technology of China (CN)
ORCID
Venue
2011 Sixth International Conference on Image and Graphics
Publisher
IEEE
Published
2011-08
Pages
760-765
DOI
10.1109/icig.2011.91
OpenAlex
W2125244676
Cited by
9
References
1
Citations by year
2017: 1 · 2016: 1 · 2015: 4 · 2014: 2 · 2013: 1
FWCI
1.53
Type
conference paper
Language
en
Open access
closed
Locations
International Conference on Image and Graphics (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
Keywords
feature point tracking, Kalman filter tracking

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