Classifying patients' affect is a pivotal part of the mental status examination. However, this common practice is often widely inconsistent between raters. Recent advances in the field of Facial Action Recognition (FAR) have enabled the development of tools that can act to identify facial expressions from videos. In this study, we aimed to explore the potential of using machine learning techniques on FAR features extracted from videotaped semi-structured psychiatric interviews of 25 male schizophrenia inpatients (mean age 41.2 years, STD = 11.4). Five senior psychiatrists rated patients' affect based on the videos. Then, a novel computer vision algorithm and a machine learning method were used to predict affect classification based on each psychiatrist affect rating. The algorithm is shown to have a significant predictive power for each of the human raters. We also found that the eyes facial area contributed the most to the psychiatrists' evaluation of the patients' affect. This study serves as a proof-of-concept for the potential of using the machine learning FAR system as a clinician-supporting tool, in an attempt to improve the consistency and reliability of mental status examination.