Nonnegative Tensor Factorization based on Low-Rank Subspace for Facial Expression Recognition


Important progresses have been made in the field of artificial intelligence in recent years, and facial expression recognition (FER), which could greatly facilitate the development of human-computer interaction, has been becoming a significant research hotspot. In this paper, a novel nonnegative tensor factorization method is proposed based on low-rank subspace (NTFLRS) for FER. Firstly, in order to find the high order correlations underlying multi-dimensional data, a data tensor model is constructed, which could represent different dimensional features ingeniously. And then, the low-rank subspace model is adopted to reconstruct the original tensor model, reduce the redundancy of the learned new tensor, and improve the discriminant abilities of inter-class information. Finally, the reconstructed tensor is decomposed to get factor matrices by nonnegative tensor factorization, where all factor matrices are used to extract subspace features. To verify the effectiveness of our proposal, two well-known facial expression datasets named as “JAFFE” and “CK+” are utilized for evaluation, and the experimental results show that the tensor-based method preserves the original structure of whole samples, which avoids the case of dimension curse because of vectorization. In addition, this method uses Laplacian graph to impose regularization on low-rank subspace model, which keeps the local relationship between sample neighbors.

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This work was supported by the National Science Foundation China under grant 61872404, the Applied Basic Research Key Programs of Science and Technology Department of Sichuan Province under the grant 2018JY0023, and the National Key Research and Development Program of China under the grant 2018YFB17002402.

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Correspondence to Xingang Liu.

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Liu, X., Li, C., Dai, C. et al. Nonnegative Tensor Factorization based on Low-Rank Subspace for Facial Expression Recognition. Mobile Netw Appl (2021).

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  • Facial expression recognition
  • Tensor representation
  • Subspace model
  • Low-rank
  • Nonnegative tensor factorization