Emotion Detection via Discriminant Laplacian Embedding
Hua Wang, Heng Huang, Fillia Makedon
Universal Access in the Information Society - 2013
Human emotion detection is of substantial importance in a variety of pervasive applications in assistive environments. Because facial expressions provide a key mechanism for understanding and conveying emotion, automatic emotion detection through facial expression recognition has attracted increased attention in both scientific research and practical applications in recent years. Traditional facial expression recognition methods normally use only one type of facial expression data, either static data extracted from one single face image or motiondependent data obtained from dynamic face image sequences, but seldom employ both. This work proposes to place the emotion detection problem under the framework of Discriminant Laplacian Embedding (DLE) to integrate these two types of facial expression data in a shared subspace, such that the advantages of both of them are exploited. Due to the reinforcement between the two types of facial features, the new data representation is more discriminative and easier to classify. Encouraging experimental results in empirical studies demonstrate the practical usage of the proposed DLE method for emotion detection.
Links
- View publications from Hua Wang
- View publications researching Embeddings
- View publications researching Graph Representations/Learning
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Cite this paper
MLA
Wang, Hua, Heng Huang, and Fillia Makedon. "Emotion detection via discriminant laplacian embedding." Universal access in the information society 13.1 (2014): 23-31.
BibTeX
@article{wang2014emotion,
title={Emotion detection via discriminant laplacian embedding},
author={Wang, Hua and Huang, Heng and Makedon, Fillia},
journal={Universal access in the information society},
volume={13},
number={1},
pages={23--31},
year={2014},
publisher={Springer}
}