The Effectiveness of Facial Cues for Automatic Detection of Cognitive Impairment Using In-the-wild Data

The development of automatic methods for the early detection of cognitive impairment (CI) has attracted much research interest due to its crucial role in helping people get suitable treatment or care. People with CI may experience various changes in their facial cues, such as eye blink rate and head movement. This thesis aims to investigate the use of facial cues to develop an automatic system for detecting CI using in-the-wild data. Firstly, the 'in-the-wild data' term is defined, and associated challenges are identified by analysing datasets used in previous work. In-the-wild data can affect the reliability of the performance of state-of-the-art approaches. Second, this thesis investigates the automatic detection of neurodegenerative disorder, mild cognitive impairment and functional memory disorder, showing the applicability of detecting health conditions with similar symptoms. Then, a novel multiple thresholds (MTs) approach for detecting an eye blink rate feature is introduced. This approach addresses in-the-wild data challenges by generating multiple thresholds, resulting in a vector of blink rates for each participant. Then, the feasibility of this feature in detecting CI is examined. Other features considered are head turn rate, head turn statistical features, head movement statistical features and low-level features. The results show that these facial features significantly distinguish different health conditions.