  Identifying Amyotrophic Lateral Sclerosis (ALS) in its early stages is
essential for establishing the beginning of treatment, enriching the outlook,
and enhancing the overall well-being of those affected individuals. However,
early diagnosis and detecting the disease's signs is not straightforward. A
simpler and cheaper way arises by analyzing the patient's facial expressions
through computational methods. When a patient with ALS engages in specific
actions, e.g., opening their mouth, the movement of specific facial muscles
differs from that observed in a healthy individual. This paper proposes Facial
Point Graphs to learn information from the geometry of facial images to
identify ALS automatically. The experimental outcomes in the Toronto Neuroface
dataset show the proposed approach outperformed state-of-the-art results,
fostering promising developments in the area.
