  POST-traumatic stress disorder (PTSD) is a chronic and debilitating mental
condition that is developed in response to catastrophic life events, such as
military combat, sexual assault, and natural disasters. PTSD is characterized
by flashbacks of past traumatic events, intrusive thoughts, nightmares,
hypervigilance, and sleep disturbance, all of which affect a person's life and
lead to considerable social, occupational, and interpersonal dysfunction. The
diagnosis of PTSD is done by medical professionals using self-assessment
questionnaire of PTSD symptoms as defined in the Diagnostic and Statistical
Manual of Mental Disorders (DSM). In this paper, and for the first time, we
collected, annotated, and prepared for public distribution a new video database
for automatic PTSD diagnosis, called PTSD in the wild dataset. The database
exhibits "natural" and big variability in acquisition conditions with different
pose, facial expression, lighting, focus, resolution, age, gender, race,
occlusions and background. In addition to describing the details of the dataset
collection, we provide a benchmark for evaluating computer vision and machine
learning based approaches on PTSD in the wild dataset. In addition, we propose
and we evaluate a deep learning based approach for PTSD detection in respect to
the given benchmark. The proposed approach shows very promising results.
Interested researcher can download a copy of PTSD-in-the wild dataset from:
http://www.lissi.fr/PTSD-Dataset/
