Fast nonnegative tensor factorization based on graph-preserving for 3D facial expression recognition

2016 ◽  
Author(s):  
Yunfang Fu ◽  
Qiuqi Ruan ◽  
Gaoyun An ◽  
Yi Jin
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Tensor Factorization ◽  
Nonnegative Tensor ◽  
Nonnegative Tensor Factorization ◽  
3D Facial Expression Recognition

scholarly journals Generalized Discriminant Orthogonal Nonnegative Tensor Factorization for Facial Expression Recognition

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Zhang XiuJun ◽  
Liu Chang
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Tensor Factorization ◽  
Nonnegative Tensor ◽  
Nonnegative Tensor Factorization ◽  
Factorization Algorithm ◽  
Factorization Algorithms ◽  
Low Dimensional ◽  
Low Dimensional Features

In order to overcome the limitation of traditional nonnegative factorization algorithms, the paper presents a generalized discriminant orthogonal non-negative tensor factorization algorithm. At first, the algorithm takes the orthogonal constraint into account to ensure the nonnegativity of the low-dimensional features. Furthermore, the discriminant constraint is imposed on low-dimensional weights to strengthen the discriminant capability of the low-dimensional features. The experiments on facial expression recognition have demonstrated that the algorithm is superior to other non-negative factorization algorithms.

scholarly journals Facial Expression Recognition Based on Discriminant Neighborhood Preserving Nonnegative Tensor Factorization and ELM

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Gaoyun An ◽  
Shuai Liu ◽  
Yi Jin ◽  
Qiuqi Ruan ◽  
Shan Lu
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition Algorithm ◽  
Expression Recognition ◽  
Tensor Factorization ◽  
Nonnegative Tensor ◽  
Neighborhood Structure ◽  
Nonnegative Tensor Factorization ◽  
Embedding Theory ◽  
Learning Machine

A novel facial expression recognition algorithm based on discriminant neighborhood preserving nonnegative tensor factorization (DNPNTF) and extreme learning machine (ELM) is proposed. A discriminant constraint is adopted according to the manifold learning and graph embedding theory. The constraint is useful to exploit the spatial neighborhood structure and the prior defined discriminant properties. The obtained parts-based representations by our algorithm vary smoothly along the geodesics of the data manifold and have good discriminant property. To guarantee the convergence, the project gradient method is used for optimization. Then features extracted by DNPNTF are fed into ELM which is a training method for the single hidden layer feed-forward networks (SLFNs). Experimental results on JAFFE database and Cohn-Kanade database demonstrate that our proposed algorithm could extract effective features and have good performance in facial expression recognition.

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