Facial Expression Recognition Using Wavelet Transform and Convolutional Neural Network

Dini Adni Navastara*, Hendry Wiranto, Chastine Fatichah, Nanik Suciati

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


A facial expression recognition is one of the machine learning applications. It categorizes an image of facial expression into one of the facial expression classes based on the extracted features from an image. Convolutional Neural Network (CNN) is one of the classification methods in which also extracts patterns from an image. In this research, we applied the CNN method to recognize facial expression. The wavelet transform is used before being processed into CNN to improve the accuracy of facial expression recognition. The facial expression images are taken from Karolinska Directed Emotional Faces (KDEF) dataset which contains seven different facial expressions. The preprocessing of the images includes converting the image to grayscale, changing the image resolution to 256 × 256 pixels, and applying data augmentation with horizontal reflection and zoom in. The experimental results of facial expression recognition using CNN with wavelet transform achieve 84.68% accuracy and without wavelet transform achieve 81.6%. The best result is 89.6% accuracy which is obtained with the data split based on the photo session, using wavelet transform, RMSprop optimizer with learning rate 0.001, and without data augmentation.

Original languageEnglish
Title of host publicationAdvances in Computer, Communication and Computational Sciences - Proceedings of IC4S 2019
EditorsSanjiv K. Bhatia, Shailesh Tiwari, Su Ruidan, Munesh Chandra Trivedi, K. K. Mishra
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9789811544088
Publication statusPublished - 2021
EventInternational Conference on Computer, Communication and Computational Sciences, IC4S 2019 - Bangkok, Thailand
Duration: 11 Oct 201912 Oct 2019

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceInternational Conference on Computer, Communication and Computational Sciences, IC4S 2019


  • Convolutional neural network
  • Data augmentation
  • Facial expression recognition
  • Wavelet transform


Dive into the research topics of 'Facial Expression Recognition Using Wavelet Transform and Convolutional Neural Network'. Together they form a unique fingerprint.

Cite this