Spontaneous Facial Expression Recognition using Sparse Representation

@inproceedings{Chanti2017SpontaneousFE,
  title={Spontaneous Facial Expression Recognition using Sparse Representation},
  author={Dawood Al Chanti and Alice Caplier},
  booktitle={VISIGRAPP},
  year={2017}
}
Facial expression is the most natural means for human beings to communicate their emotions. Most facial expression analysis studies consider the case of acted expressions. Spontaneous facial expression recognition is significantly more challenging since each person has a different way to react to a given emotion. We consider the problem of recognizing spontaneous facial expression by learning discriminative dictionaries for sparse representation. Facial images are represented as a sparse linear… 
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References

SHOWING 1-10 OF 35 REFERENCES
Robust Face Recognition via Sparse Representation
TLDR
This work considers the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise, and proposes a general classification algorithm for (image-based) object recognition based on a sparse representation computed by C1-minimization.
Research on Dynamic Facial Expressions Recognition
TLDR
A dynamic facial expression recognition method based on video sequence is proposed in this paper, which uses Gaussian of Mixture Hidden Markov Model and shows that the computing time and the error of vector quantization is reduced, while the classification efficiency is improved.
Coding facial expressions with Gabor wavelets
TLDR
The results show that it is possible to construct a facial expression classifier with Gabor coding of the facial images as the input stage and the Gabor representation shows a significant degree of psychological plausibility, a design feature which may be important for human-computer interfaces.
Face expression recognition with a 2-channel Convolutional Neural Network
A new architecture based on the Multi-channel Convolutional Neural Network (MCCNN) is proposed for recognizing facial expressions. Two hard-coded feature extractors are replaced by a single channel
Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships
  • Yan Tong, W. Liao, Q. Ji
  • Computer Science
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2007
TLDR
The experiments show that the integration of AU relationships and AU dynamics with AU measurements yields significant improvement of AU recognition, especially for spontaneous facial expressions and under more realistic environment including illumination variation, face pose variation, and occlusion.
Comprehensive database for facial expression analysis
  • T. Kanade, Ying-li Tian, J. Cohn
  • Psychology
    Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580)
  • 2000
TLDR
The problem space for facial expression analysis is described, which includes level of description, transitions among expressions, eliciting conditions, reliability and validity of training and test data, individual differences in subjects, head orientation and scene complexity image characteristics, and relation to non-verbal behavior.
Label Consistent K-SVD: Learning a Discriminative Dictionary for Recognition
TLDR
A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding and introduces a new label consistency constraint called "discriminative sparse-code error" to enforce discriminability in sparse codes during the dictionary learning process.
Application of non-negative and local non negative matrix factorization to facial expression recognition
  • I. Buciu, I. Pitas
  • Computer Science
    Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.
  • 2004
TLDR
It is found that, for the first database, LNMF outperforms both PCA and NMF, while NMF produces the poorest recognition performance, with slightly performance improvement on behalf of NMF.
DynEmo: A video database of natural facial expressions of emotions.
TLDR
DynEmo is a database of dynamic and natural emotional facial expressions displaying subjective affective states rated by both the expresser and observers containing two sets of 233 and 125 recordings of EFE of ordinary Caucasian people filmed in natural but standardized conditions.
Face detection, pose estimation, and landmark localization in the wild
TLDR
It is shown that tree-structured models are surprisingly effective at capturing global elastic deformation, while being easy to optimize unlike dense graph structures, in real-world, cluttered images.
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