摘要
在现实生活中,人们具有丰富的情感,而情感会影响人的行为及认知等。为了获取并识别人类的情感,提出一种基于深度学习的"视觉情感识别系统"的设计方案。通过Python语言编写网络爬虫程序,爬取网络上带有情感标签的人脸图片,从而为神经网络的训练提供数据;采用Keras框架搭建卷积神经网络,对带有情感标签的人脸图片进行深度学习,使卷积神经网络收敛到理想的模型,从而实现对人脸图片的情感识别。实验结果表明,该方案具有一定的识别效率。
In real life,people have a wealth of emotions which will affect people’s behavior and cognition,etc.In order to acquire and recognize human emotions,a design scheme of"visual emotion recognition system"based on deep learning is proposed.To provide data for the training of neural networks,the Python language is used to write a web crawler to crawl the face pictures with emotional tags on the network.Keras framework is adopted to build convolutional neural networks for deep learning of face images with emotion tags,so that the convolutional neural networks converges to an ideal model to realize the emotion recognition of face images.The experimental results show that the proposed scheme has certain recognition efficiency.
作者
郑浩鑫
林楷焱
陶铭
Zheng Haoxin;Lin Kaiyan;Tao Ming(School of Computer Science and Technology,Dongguan University of Technology,Dongguan,Guangdong 523808,China)
出处
《计算机时代》
2021年第3期33-36,共4页
Computer Era
基金
东莞理工学院大学生创新创业训练计划项目(国家级)
广东省自然科学基金“高密集异构无线网络移动行为认知及其在移动性管理中的应用研究”(2018A030313014)。
关键词
网络爬虫
深度学习
卷积神经网络
情感识别
web crawler
deep learning
convolutional neural networks
emotion recognition
