Selective local binary pattern with convolutional neural network for facial expression recognition

Syavira Tiara Zulkarnain, Nanik Suciati*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Variation in images in terms of head pose and illumination is a challenge in facial expression recognition. This research presents a hybrid approach that combines the conventional and deep learning, to improve facial expression recognition performance and aims to solve the challenge. We propose a selective local binary pattern (SLBP) method to obtain a more stable image representation fed to the learning process in convolutional neural network (CNN). In the preprocessing stage, we use adaptive gamma transformation to reduce illumination variability. The proposed SLBP selects the discriminant features in facial images with head pose variation using the median-based standard deviation of local binary pattern images. We experimented on the Karolinska directed emotional faces (KDEF) dataset containing thousands of images with variations in head pose and illumination and Japanese female facial expression (JAFFE) dataset containing seven facial expressions of Japanese females’ frontal faces. The experiments show that the proposed method is superior compared to the other related approaches with an accuracy of 92.21% on KDEF dataset and 94.28% on JAFFE dataset.

Original languageEnglish
Pages (from-to)6724-6735
Number of pages12
JournalInternational Journal of Electrical and Computer Engineering
Issue number6
Publication statusPublished - Dec 2022


  • Convolutional neural network
  • Facial expression recognition
  • Feature selection
  • Local binary pattern


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