[1]付倩倩,李 昂.一种改进的卷积神经网络的表情识别算法[J].计算机技术与发展,2020,30(11):80-83.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 015]
　FU Qian-qian,LI Ang.An Improved Facial Expression Recognition Technology Based on Convolutional Neural Network[J].,2020,30(11):80-83.[doi:10. 3969 / j. issn. 1673-629X. 2020. 11. 015]
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一种改进的卷积神经网络的表情识别算法()
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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11期
页码:
80-83
栏目:
智能、算法、系统工程
出版日期:
2020-11-10

文章信息/Info

Title:
An Improved Facial Expression Recognition Technology Based on Convolutional Neural Network
文章编号:
1673-629X(2020)11-0080-04
作者:
付倩倩1; 李 昂2; 3
1. 武汉邮电科学研究院,湖北 武汉 430074; 2. 南京邮电大学 通信学院,江苏 南京 210003; 3. 南京理工大学紫金学院,江苏 南京 210023
Author(s):
FU Qian-qian1; LI Ang2; 3
1. Wuhan Institute of Posts and Telecommunications,Wuhan 430074,China; 2. School of Telecommunications & Information Engineering,Nanjing University of Posts and Telecommunications, Nanjing 210003,China; 3. Nanjing University of Science and Technology Zijin College,Nanjing 210023,China
关键词:
人脸表情识别; 智能化人机交互; 深度学习; 卷积神经网络
Keywords:
facial expression recognition; intelligent human-computer interaction; deep learning; convolutional neural network
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2020. 11. 015
摘要:
随着计算机视觉领域的发展,智能化人机交互技术越来越受人们的重视。 作为最直接、最有效的情感识别方式, 人脸表情识别现已是人机交互领域研究的一大热点和难点。 由于人脸识别容易受到光照、旋转、遮挡等复杂因素的影响, 传统的人脸识别方法的准确度会大大减少。 为了使机器能够快速准确地感应人脸表情, 提出以卷积神经网络(convolutional neural network,CNN)来构建表情识别框架,将传统的人工神经网络和深度学习(deep learning,DL)技术结合起来,利用经典的卷积神经网络模型进行分析。 将表情分为愤怒、惊讶、高兴、悲伤、恐惧五大类对不同的性别进行识别与分析。 结果表明,与传统的表情识别方法相比,该方法有较好的识别效率和时效性,从而可以大大提高人机交互运用的体验感。
Abstract:
With the development of computer vision,intelligent human-computer interaction technology has been paid more and more attention. As the most direct and effective emotion recognition method, facial expression recognition has become a hot and difficult issue in the field of human-computer interaction. Because face recognition is easily affected by complex factors such? ? ? ?as illumination,rotation and occlusion,the accuracy of traditional face recognition methods will be greatly reduced. In order to make the machine can rapidly and accurately induction facial expressions, we put forward to use convolution neural network (CNN) to build a facial expression recognition framework. Combining traditional artificial neural network and deep learning? ? ? ? ?(DL),we use the classical convolution neural network model to analyze. Expressions are classified into anger,surprise,happiness,sadness and fear to identify and analyze different genders. The results show that this method has better recognition efficiency and timeliness than the traditional expression recognition method,which can greatly improve the experience of human-computer interaction.

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更新日期/Last Update: 2020-11-10