# Analysis of Facial Cues for Cognitive Decline Detection Using In-the-Wild Data  
[PERSON]  
1 Department of Computer Science, Umm Al-Qura University, Mecca 21955, Saudi Arabia 1  
influenced by factors such as education level and language barriers, leading to potential misdiagnosis. Neuroimaging can help identify structural brain changes, but it may not detect early functional changes associated with dementia. Additionally, the diagnostic process is often time-consuming, expensive, and resource-intensive, requiring specialized neurological expertise and access to advanced medical equipment. These challenges have led to growing interest in developing automated, cost-effective methods for detecting early signs of dementia and related memory impairments. Facial cues, such as eye blink rate (EBR) and head movements (HMs), have been extensively studied as indicators of mental state and cognitive health [4]. Research has demonstrated a strong correlation between these visual cues and the progression of cognitive impairment (CI), suggesting their potential as reliable biomarkers for conditions like dementia and mild cognitive impairment (MCI) [5; 6]. Recent efforts have focused on developing automated systems that leverage visual features to detect early signs of dementia [7; 8]. A key advantage of facial cue analysis is its language-independent nature. Many individuals with cognitive impairment, particularly those who have migrated and learned a second language, tend to revert to their mother tongue as their condition progresses [9]. This can pose challenges for language-based diagnostic tests, which rely heavily on verbal responses. Utilizing visual-based assessments, clinicians can overcome these limitations, ensuring more accurate and accessible dementia diagnoses across diverse linguistic and cultural populations. Previous research for dementia detection has primarily relied on data collected in controlled lab settings, where participants are recorded under optimal conditions (e.g., good lighting, fixed camera position, no background noise, fixed distance from the camera, no movements for the participants) [7; 8; 10]. While these studies have achieved high accuracy by combining language, speech, and visual features, their reliance on controlled environments limits the applicability of their work in real-world scenarios. The authors of [11] used semi-in-the-wild data, where recordings were conducted during online interviews between the patients and the doctors. However, their work is based on selected videos with good lighting and no noisy background, where the patient appears very clear to the camera. This study uses in-the-wild data and explores the discriminative potential of eye blink rate (EBR) and head movements (HMs) for identifying CI. The data include video recordings of individuals with neurodegenerative disorders (NDs), mild cognitive impairment (MCI), functional memory disorders (FMDs), and healthy controls (HCs). In this study, NDs refer to progressive neurological diseases that may lead to significant cognitive decline, such as Alzheimer's disease or frontotemporal dementia. Many forms of dementia (D) arise as clinical manifestations of NDs; thus, ND cases in this work include individuals who meet diagnostic criteria for dementia. MCI is treated as a separate group--it reflects a level of cognitive decline greater than expected for age but not severe enough to impair daily functioning or qualify as dementia. FMD refers to memory problems unrelated to neurodegenerative causes, often linked to psychological or functional causes (e.g., anxiety, stress). In this paper, \"in-the-wild\" refers to scenarios where participants are free to move naturally, with varying distances from the camera, poor lighting conditions, low video resolution, and noisy backgrounds (e.g., other people appearing in the frame). These real-world conditions present significant challenges for detecting and tracking facial cues, which rely heavily on accurate facial landmark detection.