Facial expression recognition using merged convolution neural network

Keng Cheng Liu, Chen Chien Hsu, Wei Yen Wang, Hsin Han Chiang

研究成果: 書貢獻/報告類型會議貢獻

摘要

In this paper, a merged convolution neural network (MCNN) is proposed to improve the accuracy and robustness of real-time facial expression recognition (FER). Although there are many ways to improve the performance of facial expression recognition, a revamp of the training framework and image preprocessing renders better results in applications. When the camera is capturing images at high speed, however, changes in image characteristics may occur at certain moments due to the influence of light and other factors. Such changes can result in incorrect recognition of human facial expression. To solve this problem, we propose a statistical method for recognition results obtained from previous images, instead of using the current recognition output. Experimental results show that the proposed method can satisfactorily recognize seven basic facial expressions in real time.

原文英語
主出版物標題2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁面296-298
頁數3
ISBN(電子)9781728135755
DOIs
出版狀態已發佈 - 2019 十月
事件8th IEEE Global Conference on Consumer Electronics, GCCE 2019 - Osaka, 日本
持續時間: 2019 十月 152019 十月 18

出版系列

名字2019 IEEE 8th Global Conference on Consumer Electronics, GCCE 2019

會議

會議8th IEEE Global Conference on Consumer Electronics, GCCE 2019
國家日本
城市Osaka
期間2019/10/152019/10/18

ASJC Scopus subject areas

  • Instrumentation
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Electrical and Electronic Engineering

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