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2016 | OriginalPaper | Buchkapitel

Discriminative Low-Rank Linear Regression (DLLR) for Facial Expression Recognition

verfasst von : Jie Zhu, Hao Zheng, Hong Zhao, Wenming Zheng

Erschienen in: Biometric Recognition

Verlag: Springer International Publishing

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Abstract

In this paper we focus on the need for seeking a robust low-rank linear regression algorithm for facial expression recognition. Motivated by low-rank matrix recovery, we assumed that the matrix whose data are from the same pattern as columns vectors is approximately low-rank. The proposed algorithm firstly decomposes the training images per class into the sum of the sparse error matrix, the low-rank matrix of the original images and the class discrimination criterion. Then accelerated proximal gradient algorithm was used to minimize the sum of ℓ1-norm and the nuclear matrix norm to get the set of tight linear regression base as the dictionary. Finally, we reconstruct the samples by tight dictionary and classified the face image by linear regression method according to the residual. The experimental results on facial expression databases show that the proposed method works well.

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Metadaten
Titel
Discriminative Low-Rank Linear Regression (DLLR) for Facial Expression Recognition
verfasst von
Jie Zhu
Hao Zheng
Hong Zhao
Wenming Zheng
Copyright-Jahr
2016
DOI
https://doi.org/10.1007/978-3-319-46654-5_54