Although pain is frequent in old age, older adults are often undertreated for pain. This is especially the case for longterm care residents with moderate to severe dementia who cannot report their pain because of cognitive impairments that accompany dementia. Nursing staff acknowledge the challenges of effectively recognizing and managing pain in long-term care facilities due to lack of human resources and, sometimes, expertise to use validated pain assessment approaches on a regular basis. Vision-based ambient monitoring will allow for frequent automated assessments so care staff could be automatically notified when signs of pain are displayed. However, existing computer vision techniques for pain detection are not validated on faces of older adults or people with dementia, and this population is not represented in existing facial expression datasets of pain. We present the first fully automated vision-based technique validated on a dementia cohort. First, we develop a deep learning-based computer vision system for detecting painful facial expressions on a video dataset that is collected unobtrusively from older adult participants with and without dementia. Second, we introduce a pairwise comparative inference method that calibrates to each person and is sensitive to changes in facial expression while using training data more efficiently than sequence models. Third, we introduce a fast contrastive training method that improves cross-dataset performance. Our pain estimation model outperforms baselines by a wide margin, especially when evaluated on faces of people with dementia. A. Motivation Pain is common and frequent in old age [1], but older adults are often underdiagnosed and undertreated for pain [1], [2]. This problem is especially serious for people with dementia who are often unable to verbally express or otherwise communicate their experience due to cognitive impairment [3]. Effective and validated assessment approaches, based on observation of nonverbal pain behaviours - e.g. Untreated pain can have serious physical (e.g. The motivation for this work is to develop an ambient monitoring technology to reliably, automatically, and consistently assess pain in order to improve pain management in LTC and to ultimately provide a better quality of care to older adults. B. Clinically Valid Assessments of Pain in Dementia Two clinically validated metrics for assessing pain in dementia exist: 1) the Prkachin and Solomon Pain Index (PSPI) [6] and 2) the Pain Assessment Checklist for Seniors with Limited Ability to Communicate-II (PACSLAC-II) [7]. PSPI is a 16-point metric, and is based on the Facial Action Coding System (FACS) [8].

Accurate facial expression analysis is an essential step in various clinical applications that involve physical and mental health assessments of older adults (e.g. diagnosis of pain or depression). Although remarkable progress has been achieved toward developing robust facial landmark detection methods, state-of-the-art methods still face many challenges when encountering uncontrolled environments, different ranges of facial expressions, and different demographics of the population. A recent study has revealed that the health status of individuals can also affect the performance of facial landmark detection methods on front views of faces. In this work, we investigate this matter in a much greater context using seven facial landmark detection methods. We perform our evaluation not only on frontal faces but also on profile faces and in various regions of the face. Our results shed light on limitations of the existing methods and challenges of applying these methods in clinical settings by indicating: 1) a significant difference between the performance of state-of-the-art when tested on the profile or frontal faces of individuals with vs. without dementia; 2) insights on the existing bias for all regions of the face; and 3) the presence of this bias despite re-training/fine-tuning with various configurations of six datasets.