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

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Fei, Zixiang and Yang, Erfu and Li, David Day-Uei and Butler, Stephen and Ijomah, Winifred and Zhou, Huiyu (2019) A survey on computer vision techniques for detecting facial features towards the early diagnosis of mild cognitive impairment in the elderly. Systems Science and Control Engineering, 7 (1). 252–263. ISSN 2164-2583 (https://doi.org/10.1080/21642583.2019.1647577)

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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, when most of the symptoms may not fully appear. 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. Normally, automatic facial expression recognition often involves three steps: face detection and alignment, facial feature extraction and facial feature classification. 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.

ORCID iDs

Fei, Zixiang, Yang, Erfu ORCID logoORCID: https://orcid.org/0000-0003-1813-5950, Li, David Day-Uei ORCID logoORCID: https://orcid.org/0000-0002-6401-4263, Butler, Stephen ORCID logoORCID: https://orcid.org/0000-0002-2103-0773, Ijomah, Winifred and Zhou, Huiyu;
CORE (COnnecting REpositories)