  The prevalence of suicide has been on the rise since the 20th century,
causing severe emotional damage to individuals, families, and communities
alike. Despite the severity of this suicide epidemic, there is so far no
reliable and systematic way to assess suicide intent of a given individual.
Through efforts to automate and systematize diagnosis of mental illnesses over
the past few years, verbal and acoustic behaviors have received increasing
attention as biomarkers, but little has been done to study eyelids, gaze, and
head pose in evaluating suicide risk. This study explores statistical analysis,
feature selection, and machine learning classification as means of suicide risk
evaluation and nonverbal behavioral interpretation. Applying these methods to
the eye and head signals extracted from our unique dataset, this study finds
that high-risk suicidal individuals experience psycho-motor retardation and
symptoms of anxiety and depression, characterized by eye contact avoidance,
slower blinks and a downward eye gaze. By comparing results from different
methods of classification, we determined that these features are highly capable
of automatically classifying different levels of suicide risk consistently and
with high accuracy, above 98%. Our conclusion corroborates psychological
studies, and shows great potential of a systematic approach in suicide risk
evaluation that is adoptable by both healthcare providers and naive observers.

Signal Processing