Mutual-Information-based Feature Selection for Facial Emotion Recognition on Light-Weight Devices

Yingjun Dong, Hiroki Sayama

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

Abstract

Light-weight devices have become ubiquitous in our daily life, such as smartphones, smart monitors, and other smart devices in our home. As light-weight devices are becoming popular, the demand for sophisticated human-computer interaction (HCI) applications for light-weight devices is also increasing. One particularly promising HCI application for light-weight devices is facial expression recognition (FER), since it may open up possibilities of various medical, psychological or psychiatric monitoring. However, its high computational demand has prevented widespread adoption of FER on light-weight devices. To address this issue, here we aim at decreasing computational overhead of FER by reducing the number of facial landmarks. We calculated mutual information of facial landmarks' movements and detected their clusters using hierarchical agglomerative clustering (HAC). We also applied a genetic algorithm (GA)-inspired landmark selection method to filter out low-utility features from each facial landmark cluster. The selected features were provided to a support vector machine (SVM) classifier to classify facial expressions, and its performance was compared among several different algorithm settings. Results showed that our proposed method achieved classification accuracy similar to the classifier that used the original full-featured dataset, with improved performance robustness and computational time reduced by 63.5%.

Original languageEnglish
Title of host publication2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2455-2461
Number of pages7
ISBN (Electronic)9781728124858
DOIs
Publication statusPublished - 2019 Dec
Externally publishedYes
Event2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019 - Xiamen, China
Duration: 2019 Dec 62019 Dec 9

Publication series

Name2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019

Conference

Conference2019 IEEE Symposium Series on Computational Intelligence, SSCI 2019
CountryChina
CityXiamen
Period19/12/619/12/9

Keywords

  • facial expression recognition
  • feature selection
  • hierarchical agglomerative clustering
  • light-weight devices
  • mutual information
  • support vector machine

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Modelling and Simulation

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