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


Ontology type: schema:Chapter     


Chapter Info

DATE

2016

AUTHORS

Jie Zhu , Hao Zheng , Hong Zhao , Wenming Zheng

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. More... »

PAGES

494-502

References to SciGraph publications

Book

TITLE

Biometric Recognition

ISBN

978-3-319-46653-8
978-3-319-46654-5

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-46654-5_54

DOI

http://dx.doi.org/10.1007/978-3-319-46654-5_54

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1084849850


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