[1]林巧民,潘 敏.基于模糊聚类的“愤怒"表情细分方法研究[J].计算机技术与发展,2020,30(01):44-49.[doi:10. 3969 / j. issn. 1673-629X. 2020. 01. 008]
　LIN Qiao-min,PAN Min.Research on“Angry” Expression Subdivision Method Based on Fuzzy Clustering[J].Computer Technology and Development,2020,30(01):44-49.[doi:10. 3969 / j. issn. 1673-629X. 2020. 01. 008]
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基于模糊聚类的“愤怒"表情细分方法研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
30
期数:
2020年01期
页码:
44-49
栏目:
智能、算法、系统工程
出版日期:
2020-01-10

文章信息/Info

Title:
Research on“Angry” Expression Subdivision Method Based on Fuzzy Clustering
文章编号:
1673-629X(2020)01-0044-06
作者:
林巧民1; 2 ; 潘 敏1
1. 南京邮电大学 计算机学院,江苏 南京 210023; 2. 南京邮电大学 教育科学与技术学院,江苏 南京 210003
Author(s):
LIN Qiao-min 1; 2 ; PAN Min 1
1. School of Computer Science,Nanjing University of Posts &Telecommunications,Nanjing 210023,China; 2. School of Educational Science and Technology,Nanjing University of Posts &Telecommunications, Nanjing 210003,China
关键词:
情感计算; ANFIS; 聚类算法; RBF 神经网络
Keywords:
emotional calculation; ANFIS; clustering algorithm; RBF neural network
分类号:
TP391.9
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 01. 008
摘要:
伴随情感计算和人机交互界面的快速发展,计算机的情感识别能力受到越来越多的关注。近年来针对面部表情识别存在很多方法,然而对于表情层次的细分研究却不多。 目前网约车司机以及公交乘客的情绪失控情况无法被摄像头监控系统及时检测,该研究有助于此问题的解决。文中针对“愤怒冶表情进行层次细分研究。首先通过 RBF 神经网络进行大类情绪识别,然后从已识别‘愤怒’情绪的多帧视频图像样本中选取出部分连续的图像样本。 接着把选取出的连续样本进行融合聚类,确定初始聚类中心个数。 最后通过自适应神经模糊推理系统(adaptive neural-based fuzzy inference system,ANFIS)对识别出的愤怒表情进行打分,分值越高愤怒程度越高。 创新点在于情绪样本图片的选取上,基于人的情绪是有一定生成过程,选取同一个人“愤怒冶时的连续多张图片作为样本。 通过实验结果证明了该方法的有效性。
Abstract:
With the rapid development of emotional computing and human-computer interaction interface, more and more attention has been paid to the emotion recognition ability of computers. In recent years,there are many methods for facial expression recognition,but few subdivisions of expression level. At present,the emotional disorder of the network driver and the bus passenger cannot be detected by the camera monitoring system in time,so this research helps to solve this problem. The hierarchical analysis of “angry” expressions is carried out. First,a large class of emotion recognition is performed through the RBF neural network,and then a part of consecutive imagesamples are selected from the multi-frame video image samples that have been identified as “angry” emotions. Then,the selected continuous samples are subjected to fusion clustering to determine the number of initial cluster centers. Finally,the identified angry expressions are scored by the adaptive neural-based fuzzy inference system (ANFIS). The higher the score,the higher the degree of anger. The innovation lies in the selection of emotional sample pictures. Based on the human emotion,there is a certain generation process. The consecutive multiple pictures of the same person “angry” are selected as samples. According to the experiment,the methodproposed is effective.

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更新日期/Last Update: 2020-01-10