Intelligent facial emotion recognition based on stationary wavelet entropy and Jaya algorithm

Shui Hua Wang, Preetha Phillips, Zheng Chao Dong, Yu Dong Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

154 Citations (Scopus)


Aim Emotion recognition based on facial expression is an important field in affective computing. Current emotion recognition systems may suffer from two shortcomings: translation in facial image may deteriorate the recognition performance, and the classifier is not robust. Method To solve above two problems, our team proposed a novel intelligent emotion recognition system. Our method used stationary wavelet entropy to extract features, and employed a single hidden layer feedforward neural network as the classifier. To prevent the training of the classifier fall into local optimum points, we introduced the Jaya algorithm. Results The simulation results over a 20-subject 700-image dataset showed our algorithm reached an overall accuracy of 96.80 ± 0.14%. Conclusion This proposed approach performs better than five state-of-the-art approaches in terms of overall accuracy. Besides, the db4 wavelet performs the best among other whole db wavelet family. The 4-level wavelet decomposition is superior to other levels. In the future, we shall test other advanced features and training algorithms.

Original languageEnglish
Pages (from-to)668-676
Number of pages9
Publication statusPublished - 10 Jan 2018
Externally publishedYes


  • Affective computing
  • Emotion recognition
  • Facial expression
  • Feedforward neural network
  • Jaya algorithm
  • Optimal decomposition level
  • Optimal wavelet
  • Single hidden layer
  • Stationary wavelet entropy


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