Faces are Domains: Domain Incremental Learning for Expression Recognition

Rahul Singh Maharjan, Marta Romeo, Angelo Cangelosi

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Since most existing facial expression recognition methods depend on deep learning models trained in isolation on a facial expression image corpora, once employed in scenarios that are different from those in the corpora, they usually demand ad-hoc retraining to be able to perform better in the expression recognition task for new scenarios. Furthermore, most of these facial expression recognition methods are inconsistent when recognising person-specific expressions or are incapable of adjusting to real-world scenarios where data is exclusively obtainable incrementally. In this paper, we present a face incremental expression recognition model, where we utilise domain incremental learning methods to learn individual facial features of facial expressions. We assume that each individual's facial expression (domain) is presented to the model one domain at a time. We assessed our model's ability to remember previously seen domains (individual's facial expression) and incrementally perform on new face domains. Our model improves performance compared to a non-incremental learning model and an incremental learning model in facial expression recognition for individual data with different expression classes.
Original languageEnglish
Title of host publication2023 International Joint Conference on Neural Networks (IJCNN)
ISBN (Electronic)9781665488679
Publication statusPublished - 2 Aug 2023
Event2023 International Joint Conference on Neural Networks - Gold Coast, Australia
Duration: 18 Jun 202323 Jun 2023


Conference2023 International Joint Conference on Neural Networks


  • Affective Computing
  • Deep Learning
  • Facial Expression Recognition
  • Incremental Learning

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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