Dual subspace nonnegative matrix factorization for person-invariant facial expression recognition

Person-dependent appearance changes tend to increase difficulties in automatic facial expression recognition. Although one can use neutral face images to reduce the personal variations, acquisition of neutral face images may not always be possible in real cases. In order to remove the person-depende...

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Bibliographic Details
Published inProceedings of the 21st International Conference on Pattern Recognition (ICPR2012) pp. 2391 - 2394
Main Authors Yi-Han Tu, Chiou-Ting Hsu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2012
Subjects
Face
Face recognition
Feature extraction
Image reconstruction
Matrix decomposition
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Summary:Person-dependent appearance changes tend to increase difficulties in automatic facial expression recognition. Although one can use neutral face images to reduce the personal variations, acquisition of neutral face images may not always be possible in real cases. In order to remove the person-dependent influence from expressive images, we propose a dual subspace nonnegative matrix factorization (DSNMF) to decompose facial images into two parts: identity and expression parts. The identity part should characterize person-dependent variations, while the expression part should characterize person-invariant expression features. Our experimental results show that the proposed method significantly outperforms existing approaches on the CK+ and JAFFE expression databases.
ISBN:9781467322164
1467322164
ISSN:1051-4651
2831-7475