  This paper reports the analysis of audio and visual features in predicting
the continuous emotion dimensions under the seventh Audio/Visual Emotion
Challenge (AVEC 2017), which was done as part of a B.Tech. 2nd year internship
project. For visual features we used the HOG (Histogram of Gradients) features,
Fisher encodings of SIFT (Scale-Invariant Feature Transform) features based on
Gaussian mixture model (GMM) and some pretrained Convolutional Neural Network
layers as features; all these extracted for each video clip. For audio features
we used the Bag-of-audio-words (BoAW) representation of the LLDs (low-level
descriptors) generated by openXBOW provided by the organisers of the event.
Then we trained fully connected neural network regression model on the dataset
for all these different modalities. We applied multimodal fusion on the output
models to get the Concordance correlation coefficient on Development set as
well as Test set.
