A survey on computer vision techniques for detecting facial features towards the early diagnosis of mild cognitive impairment in the elderly

Zixiang Fei, Erfu Yang*, David Day Uei Li, Stephen Butler, Winifred Ijomah, Huiyu Zhou

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

Abstract

In the UK, more and more people are suffering from various kinds of cognitive impairment. Its early detection and diagnosis can be of great importance. However, it is challenging to detect cognitive impairment in the early stage with high accuracy and low costs. Some currently popular methods include cognitive tests and neuroimaging techniques which have their own drawbacks. Whilst viewing videos, studies have shown that the facial expressions of people with cognitive impairment exhibit abnormal corrugator activities compared to those without cognitive impairment. The aim of this paper is to explore promising computer vision and pattern analysis techniques in the case of detecting cognitive impairment through facial expression analysis. This paper presents a survey of computer vision techniques to detect facial features for early diagnosis of cognitive impairment. Additionally, this paper reviews and compares the advantages and disadvantages of such techniques. Automatic facial expression analysis has the potential to be used for cognitive impairment detection in the elderly. In the case of detecting cognitive impairment through facial expression analysis, it may be better to use a local method of facial components alignment, and employ static approaches in facial feature extraction and facial feature classification.

Original languageEnglish
Pages (from-to)252-263
Number of pages12
JournalSystems Science and Control Engineering
Volume7
Issue number1
DOIs
Publication statusPublished - 31 Jul 2019
Externally publishedYes

Bibliographical note

Funding Information:
This research is funded by CAPITA plc in Strathclyde?s Strategic Technology Partnership (STP) Programme. Huiyu Zhou was partly funded by Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/N011074/1, and Royal Society?in?Newton Advanced Fellowship under Grant NA160342. David Day-Uei Li was funded by Engineering and Physical Sciences Research Council (EPSRC) with project code EP/M506643/1. The authors thank Dr Neil Mackin (CAPITA mentor) and Miss Angela Anderson (the STP?s coordinator) for their support. The contents including any opinions and conclusions made in this paper are those of the authors alone. They do not necessarily represent the views of CAPITA plc.

Funding Information:
This research is funded by CAPITA plc in Strathclyde’s Strategic Technology Partnership (STP) Programme . Huiyu Zhou was partly funded by Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/N011074/1, and Royal Society in Newton Advanced Fellowship under Grant NA160342.David Day-Uei Li was funded by Engineering and Physical Sciences Research Council (EPSRC) with project code EP/M506643/1.

Publisher Copyright:
© 2019, © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

Keywords

  • cognitive impairment
  • computer vision techniques
  • Facial features analysis
  • literature review

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Control and Optimization
  • Artificial Intelligence

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