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基于多姿态多状态面部情绪模型的表情识别
引用本文:陈国社,张青,李凡.基于多姿态多状态面部情绪模型的表情识别[J].华中科技大学学报(自然科学版),2004,32(8):60-62.
作者姓名:陈国社  张青  李凡
作者单位:华中科技大学,计算机科学与技术学院,湖北,武汉,430074;黄冈师范学院,物理与电子技术系,湖北,黄冈,436100
基金项目:国家高性能计算基金资助项目 (0 0 30 3)
摘    要:介绍了面部表情识别的主要步骤,在此基础上提出了一种多姿态多状态的面部情绪模型.该系统利用图像质量的评价结果来决定面部检测的方法,通过中性脸的检测来实现表情边界的分割,从而把提取的表情运动特征参数作为BP神经网络的输入,进而实现面部表情的识别.实验表明,该系统具有很好的识别效果.

关 键 词:面部表情识别  面部检测  表情特征区域  多姿态多状态面部情绪模型
文章编号:1671-4512(2004)08-0060-03
修稿时间:2003年12月26

A method for the recognition of a variety of facial expression
Chen Guoshe Postgraduate, College of Computer Sci. & Tech.,Huazhong Univ. of Sci. & Tech.,Wuhan ,China. Zhang Qing Li Fan.A method for the recognition of a variety of facial expression[J].JOURNAL OF HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY.NATURE SCIENCE,2004,32(8):60-62.
Authors:Chen Guoshe Postgraduate  College of Computer Sci & Tech  Huazhong Univ of Sci & Tech  Wuhan  China Zhang Qing Li Fan
Institution:Chen Guoshe Postgraduate, College of Computer Sci. & Tech.,Huazhong Univ. of Sci. & Tech.,Wuhan 430074,China. Zhang Qing Li Fan
Abstract:The primary steps of facial expression recognition were introduced; the multi pose and multi state facial emotion models were proposed. The method of face detection by using the result of image quality estimation was selected. Segmentation of expression border was realized by the detection of neural face. By taking the feature parameters of the extracted expression motion as the input of BP neural network, the facial expression was recognized. The results showed that the system was characterized by good recognition.
Keywords:facial expression recognition  face detection  expression feature area  multi-pose and multi-state facial emotion model
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