Multiple Convolutional Neural Networks fusion using improved fuzzy integral for facial emotion recognition

Cheng Jian Lin, Chun Hui Lin, Shyh Hau Wang, Chen Hsien Wu

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Facial expressions are indispensable in human cognitive behaviors since it can instantly reveal human emotions. Therefore, in this study, Multiple Convolutional Neural Networks using Improved Fuzzy Integral (MCNNs-IFI) were proposed for recognizing facial emotions. Since effective facial expression features are difficult to design; deep learning CNN is used in the study. Each CNN has its own advantages and disadvantages, thus combining multiple CNNs can yield superior results. Moreover, multiple CNNs combined with improved fuzzy integral, in which its fuzzy density value is optimized through particle swarm optimization (PSO), overcomes the majority decision drawback in the traditional voting method. Two Multi-PIE and CK+ databases and three main CNN structures, namely AlexNet, GoogLeNet, and LeNet, were used in the experiments. To verify the results, a cross-validation method was used, and experimental results indicated that the proposed MCNNs-IFI exhibited 12.84% higher accuracy than that of the three CNNs.

Original languageEnglish
Article number2593
JournalApplied Sciences (Switzerland)
Volume9
Issue number13
DOIs
Publication statusPublished - 2019 Jul 1

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

Fingerprint Dive into the research topics of 'Multiple Convolutional Neural Networks fusion using improved fuzzy integral for facial emotion recognition'. Together they form a unique fingerprint.

Cite this