# Detection of Mild Cognitive Impairment Using Facial Features in Video Conversations

arXiv: https://arxiv.org/abs/2308.15624 · HTML: https://mlsanity.com/paper/2308.15624/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 54 references that are in the map.
- First posted on arXiv 29 Aug 2023.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [ViViT: A Video Vision Transformer](https://mlsanity.com/paper/2103.15691/) (2021)
- [Character Region Awareness for Text Detection](https://mlsanity.com/paper/1904.01941/) (2019)
- [End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design](https://mlsanity.com/paper/1711.05775/) (2017)
- [MC-ViViT: Multi-branch Classifier-ViViT to detect Mild Cognitive Impairment in older adults using facial videos](https://mlsanity.com/paper/2304.05292/) (2023)
- [Limitations and Biases in Facial Landmark Detection -- An Empirical Study on Older Adults with Dementia](https://mlsanity.com/paper/1905.07446/) (2019)
- [Deep Convolution Network Based Emotion Analysis for Automatic Detection of Mild Cognitive Impairment in the Elderly](https://mlsanity.com/paper/2111.05066/) (2021)

## Built on by (most cited first, 5 of 5)

- [A Review of Deep Learning Approaches for Non-Invasive Cognitive Impairment Detection](https://mlsanity.com/paper/2410.19898/) (2024)
- [Linguistic-Based Mild Cognitive Impairment Detection Using Informative Loss](https://mlsanity.com/paper/2402.01690/) (2024)
- [Cross-Enhanced Multimodal Fusion of Eye-Tracking and Facial Features for Alzheimer's Disease Diagnosis](https://mlsanity.com/paper/2510.24777/) (2025)
- [Contrastive learning-based video quality assessment-jointed video vision transformer for video recognition](https://mlsanity.com/paper/2603.10965/) (2026)
- [TAMI: Temporally Aligned, Missingness-Aware, and Interpretable Multimodal Fusion for Mental Health Assessment in Older Adults with Mild Cognitive Impairment](https://mlsanity.com/paper/2608.30857/) (2026)

## Abstract (arXiv)

Early detection of Mild Cognitive Impairment (MCI) leads to early interventions to slow the progression from MCI into dementia. Deep Learning (DL) algorithms could help achieve early non-invasive, low-cost detection of MCI. This paper presents the detection of MCI in older adults using DL models based only on facial features extracted from video-recorded conversations at home. We used the data collected from the I-CONECT behavioral intervention study (NCT02871921), where several sessions of semi-structured interviews between socially isolated older individuals and interviewers were video recorded. We develop a framework that extracts spatial holistic facial features using a convolutional autoencoder and temporal information using transformers. Our proposed DL model was able to detect the I-CONECT study participants' cognitive conditions (MCI vs. those with normal cognition (NC)) using facial features. The segments and sequence information of the facial features improved the prediction performance compared with the non-temporal features. The detection accuracy using this combined method reached 88% whereas 84% is the accuracy without applying the segments and sequences information of the facial features within a video on a certain theme.