Deep Learning for Illumination Invariant Facial Expression Recognition

Ariel Ruiz-Garcia, Vasile Palade, Mark Elshaw, Ibrahim Almakky

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

4 Citations (Scopus)

Abstract

In this work we propose a novel method to address illumination invariance for facial expression recognition. We propose a Deep Convolutional Network (CNN) pre-trained as a Deep Stacked Convolutional Autoencoder (SCAE) in a greedy layer-wise unsupervised fashion. The SCAE model learns to encode facial expression images and produce a feature vector with relatively similar illumination, regardless of the luminance level of the input image. Moreover, we propose fine-tuning the stacked shallow autoencoders after each one of these is trained greedily, rather than just at the end, and show that this approach significantly improves the set of illumination invariant features learnt by the SCAE. Finally, we propose the use of a variant rectifier linear unit transfer function that helps the SCAE model reduce or increase the illumination of images with high or low luminance, and show that the lower and upper bounds greatly influence classification performance. The method proposed provides an increase in classification accuracy of 4% on the KDEF dataset and 8% on the CK+ dataset.

Original languageEnglish
Title of host publication2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2018-July
ISBN (Electronic)978-1-5090-6014-6
ISBN (Print)978-1-5090-6015-3
DOIs
Publication statusPublished - 10 Oct 2018
Event2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brazil
Duration: 8 Jul 201813 Jul 2018

Conference

Conference2018 International Joint Conference on Neural Networks, IJCNN 2018
CountryBrazil
CityRio de Janeiro
Period8/07/1813/07/18

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Ruiz-Garcia, A., Palade, V., Elshaw, M., & Almakky, I. (2018). Deep Learning for Illumination Invariant Facial Expression Recognition. In 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings (Vol. 2018-July). [8489123] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2018.8489123