1Assistant Professor, Department of Electronics and Communication Engineering, Siksha'O’ Anusandhan (Deemed To Be University), Faculty Of Engineering., Odisha, India
2Professor, Department of Electronics and Communication Engineering, Siksha'O’ Anusandhan (Deemed To Be University), Faculty Of Engineering., Odisha, India
*Corresponding Author: Niva Das Professor, Department of Computer ECE, Siksha'O ’Anusandhan (Deemed To Be University), Faculty of Engineering, Odisha, India Email: nivadas@soa.ac.in
Online published on 21 January, 2019.
Depression has turned out to be one of the leading causes of disability according to a report by world health organization. It has become a major contributor to the global health hazard. More than 300 million people of all ages suffer from depression globally. According to researchers, the incidence of depression is on the rise as it remains under diagnosed and under treated. Due to lack of knowledge many a times, symptoms like sleeplessness, aches, pains and lack of energy are left untreated. As diagnosis is not so straightforward, treatment gets delayed. With the advances in research as the problem of depression is better understood; new approaches for treatment, detection and prevention are developing. This paper presents a short review of the state of the art techniques for detecting depression at an early stage.
Depression, Landmark Distribution Model, Facial Action Coding Systems (FACs), Active Appearance Model (AAM), Support Vector Machine (SVM), Gaussian Mixture Model (GMM)
