期刊论文

Yi, Jizheng;Chen, Aibin

英文

Applied Soft Computing

1568-4946

2019

82

105540

10.1016/j.asoc.2019.105540

The authors would like to thank all editors and the anonymous reviewers for their valuable suggestions. This paper has utilized BHU facial expression database and MMI facial expression database. The authors would like to thank the providers of these databases. This work was supported in part by the National Natural Science Foundation of China (Grant no. 61602528 and Grant no. 61772561), the Hunan Provincial Natural Science Foundation of China (Grant no. 2017JJ3527), the Scientific Research Fund of Hunan Provincial Education Department, China (Grant no. 16B275), the Grant of China Scholarship Council (CSC no. 201808430002), and the Research Foundation for Advanced Talents of Central South University of Forestry and Technology, China (Grant no. 2015YJ013).

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Facial Expression Recognition (FER) is an important subject of human–computer interaction and has long been a research area of great interest. Accurate Facial Expression Sequence Interception (FESI) and discriminative expression feature extraction are two enormous challenges for the video-based FER. This paper proposes a framework of FER for the intercepted video sequences by using feature point movement trend and feature block texture variation. Firstly, the feature points are marked by Active Appearance Model (AAM) and the most representativ...