A Multi-Population FA for Automatic Facial Emotion Recognition. / Mistry, Kamlesh; Rizvi, Baqar ; Rook, Chris; Iqbal, Sadaf ; Zhang, Li; Joy, Colin Paul .

A Multi-Population FA for Automatic Facial Emotion Recognition. International Joint Conference on Neural Networks (IJCNN) : IEEE, 2020.

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

Published
  • Kamlesh Mistry
  • Baqar Rizvi
  • Chris Rook
  • Sadaf Iqbal
  • Li Zhang
  • Colin Paul Joy

Abstract

Automatic facial emotion recognition system is popular in various domains such as health care, surveillance and human-robot interaction. In this paper we present a novel multi-population FA for automatic facial emotion recognition. The overall system is equipped with horizontal vertical neighborhood local binary patterns (hvnLBP) for feature extraction, a novel multi-population FA for feature selection and diverse classifiers for emotion recognition. First, we extract features using hvnLBP, which are robust to illumination changes, scaling and rotation variations. Then, a novel FA variant is proposed to further select most important and emotion specific features. These selected features are used as input to the classifier to further classify seven basic emotions. The proposed system is evaluated with multiple facial expression datasets and also compared with other state-of-the-art models.
Original languageEnglish
Title of host publicationA Multi-Population FA for Automatic Facial Emotion Recognition
Place of PublicationInternational Joint Conference on Neural Networks (IJCNN)
PublisherIEEE
ISBN (Electronic)9781728169279
ISBN (Print)9781728169262
DOIs
Publication statusPublished - 28 Sep 2020
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 43380049