Heechul Jung, Sihaeng Lee, Sunjeong Park, Byungju Kim, Junmo Kim, Injae Lee, C. Ahn
{"title":"Development of deep learning-based facial expression recognition system","authors":"Heechul Jung, Sihaeng Lee, Sunjeong Park, Byungju Kim, Junmo Kim, Injae Lee, C. Ahn","doi":"10.1109/FCV.2015.7103729","DOIUrl":null,"url":null,"abstract":"Deep learning is considered to be a breakthrough in the field of computer vision, since most of the world records of the recognition tasks are being broken. In this paper, we try to apply such deep learning techniques to recognizing facial expressions that represent human emotions. The procedure of our facial expression recognition system is as follows: First, face is detected from input image using Haar-like features. Second, the deep network is used for recognizing facial expression using detected faces. In this step, two different deep networks can be used such as deep neural network and convolutional neural network. Consequently, we compared experimentally two types of deep networks, and the convolutional neural network had better performance than deep neural network.","PeriodicalId":424974,"journal":{"name":"2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"51","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 21st Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FCV.2015.7103729","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 51
Abstract
Deep learning is considered to be a breakthrough in the field of computer vision, since most of the world records of the recognition tasks are being broken. In this paper, we try to apply such deep learning techniques to recognizing facial expressions that represent human emotions. The procedure of our facial expression recognition system is as follows: First, face is detected from input image using Haar-like features. Second, the deep network is used for recognizing facial expression using detected faces. In this step, two different deep networks can be used such as deep neural network and convolutional neural network. Consequently, we compared experimentally two types of deep networks, and the convolutional neural network had better performance than deep neural network.