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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Zhu, J., Zheng, H., Zhao, H., Zheng, W. (2016). Discriminative Low-Rank Linear Regression (DLLR) for Facial Expression Recognition. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_54
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DOI: https://doi.org/10.1007/978-3-319-46654-5_54
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