Emotion Recognition of Facial Expression Using Convolutional Neural Network

被引:4
作者
Kumar, Pradip [1 ]
Kishore, Ankit [1 ]
Pandey, Raksha [1 ]
机构
[1] Guru Ghasidas Vishwavidyalaya, Dept CSE SoS Engn & Technol, Bilaspur, CG, India
来源
INNOVATIVE DATA COMMUNICATION TECHNOLOGIES AND APPLICATION | 2020年 / 46卷
关键词
Human facial emotion recognition; Facial expression recognition; Face recognition and Human-Computer interaction (HCI); CNN;
D O I
10.1007/978-3-030-38040-3_41
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
Recognition of emotion using facial information is an interesting field for computer science, medicine, and psychology. Various researches are working with automated facial expression recognition system. Convolutional neural network (CNN) for facial emotion recognition method is basically used to recognize different-different human facial landmarks, geometrical poses, and emotions of faces. Human facial expression gives very important information to understand the emotions of a person for an interpersonal relationship. Since it is a classification problem, so the performance of any classifier is dependent on features extracted from the region of interest of the sample. In this paper, we are going to train the machine to recognize different types of emotions through human facial expressions using the Convolutional Neural Network (CNN). We have used sequential forward selection algorithms and softmax activation function.
引用
收藏
页码:362 / 369
页数:8
相关论文
共 12 条
[1]   Covariance Pooling for Facial Expression Recognition [J].
Acharya, Dinesh ;
Huang, Zhiwu ;
Paudel, Danda Pani ;
Van Gool, Luc .
PROCEEDINGS 2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS (CVPRW), 2018, :480-487
[2]  
[Anonymous], INTRO EMOTION RECOGN
[3]  
[Anonymous], 2010, IEEE COMPUTER SOC C
[4]  
[Anonymous], J MACH LEARN RES
[5]   Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution [J].
Barsoum, Emad ;
Zhang, Cha ;
Ferrer, Cristian Canton ;
Zhang, Zhengyou .
ICMI'16: PROCEEDINGS OF THE 18TH ACM INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION, 2016, :279-283
[6]  
Burkert P., 2015, IEEE C VEH EL SAF IC
[7]  
Glauner P.O., 2017, FLINS C
[8]  
Kim M.H., 2005, INT C CONTR AUT SYST
[9]  
Lin S.-H, INTRO FACE RECOGNITI
[10]  
OShea K, 2015, INTRO CONVOLUTIONAL