Facial Expression Recognition.pdfVIP

Facial Expression Recognition Based on Discriminant Neighborhood Preserving Non-Negative Tensor Factorization and ELM Gaoyun An 1 , Shuai Liu 1,2 , Yi Jin 1 , Qiuqi Ruan 1 , Shan Lu 3 1 Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China 2 Baidu Online Network Technology (Beijing) Co .Ltd, Beijing 100085, China 3 School of Information, Renmin University of China, Beijing 100872, China Abstract In this paper, a novel facial expression recognition algorithm based on discriminant neighborhood preserving non-negative 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. Keywords: Non-negative tensor factorization, Neighborhood preserving, Extreme learning machine, Facial expression recognition 1. Introduction Facial expression recognition plays an important role in human-computer interaction, and 55% information is transferred by facial expression in face-to-face human communication [1]. Although many methods were proposed, recognizing facial expression is still challenging due to the complex, variable, and subtle facial expressions. One of the effective methods for facial expression recognition is