  Depression is a major mental health disorder that is rapidly affecting lives
worldwide. Depression not only impacts emotional but also physical and
psychological state of the person. Its symptoms include lack of interest in
daily activities, feeling low, anxiety, frustration, loss of weight and even
feeling of self-hatred. This report describes work done by us for Audio Visual
Emotion Challenge (AVEC) 2017 during our second year BTech summer internship.
With the increase in demand to detect depression automatically with the help of
machine learning algorithms, we present our multimodal feature extraction and
decision level fusion approach for the same. Features are extracted by
processing on the provided Distress Analysis Interview Corpus-Wizard of Oz
(DAIC-WOZ) database. Gaussian Mixture Model (GMM) clustering and Fisher vector
approach were applied on the visual data; statistical descriptors on gaze,
pose; low level audio features and head pose and text features were also
extracted. Classification is done on fused as well as independent features
using Support Vector Machine (SVM) and neural networks. The results obtained
were able to cross the provided baseline on validation data set by 17% on audio
features and 24.5% on video features.
