Abstract
Depression is a serious mood disorder that can significantly impact a person’s ability to live a normal life. In severe cases, it can even lead to suicidal thoughts. As such, accurate detection of depression is crucial for effective management and treatment. This paper presents a facial expression-based approach for depression detection, which is composed of two steps. First, static features are extracted using Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG), and Bag of Words (BOW). Second, dynamic features are obtained by applying LBPs on Three Orthogonal Planes (LBP-TOP) and Eight Vertices LBP (EVLBP) frame by frame. Next, the static and dynamic features are combined to create a 1377-dimensional vector for each video. Finally, Gradient Boosting Regression is used to predict depression scores. The experimental results on the AVEC 2014 depression dataset ([Formula: see text], [Formula: see text]) demonstrate the effectiveness of the proposed method. These results indicate that the low-dimensional vectors extracted by the proposed method can effectively capture the facial motion of individuals with depression, and also suggest that hand-crafted methods could have potential in depression detection.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
