Learning inhomogeneous Gibbs model of faces by minimax entropy
Learning inhomogeneous Gibbs model of faces by minimax entropy
- New search for: Ce Liu,
- New search for: Song Chun Zhu,
- New search for: Heung-Yeung Shum,
- New search for: Ce Liu,
- New search for: Song Chun Zhu,
- New search for: Heung-Yeung Shum,
In this paper we propose a novel inhomogeneous Gibbs model by the minimax entropy principle, and apply it to face modeling. The maximum entropy principle generalizes the statistical properties of the observed samples and results in the Gibbs distribution, while the minimum entropy principle makes the learnt distribution close to the observed one. To capture the fine details of a face, an inhomogeneous Gibbs model is derived to learn the local statistics of facial feature paints. To alleviate the high dimensionality problem of face models, we propose to learn the distribution in a subspace reduced by principal component analysis or PCA. We demonstrate that our model effectively captures important and subtle non-Gaussian face patterns and efficiently generates good face models.
Document information
Learning inhomogeneous Gibbs model of faces by minimax entropy
Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 ; 1 ; 281-287 vol.1
2001-01-01
616675 byte
Conference paper
Electronic Resource
English
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