  As we exceed upon the procedures for modelling the different aspects of
behaviour, expression recognition has become a key field of research in Human
Computer Interactions. Expression recognition in the wild is a very interesting
problem and is challenging as it involves detailed feature extraction and heavy
computation. This paper presents the methodologies and techniques used in our
contribution to recognize different expressions i.e., neutral, anger, disgust,
fear, happiness, sadness, surprise in ABAW competition on Aff-Wild2 database.
Aff-Wild2 database consists of videos in the wild labelled for seven different
expressions at frame level. We used a bi-modal approach by fusing audio and
visual features and train a sequence-to-sequence model that is based on Gated
Recurrent Units (GRU) and Long Short Term Memory (LSTM) network. We show
experimental results on validation data. The overall accuracy of the proposed
approach is 41.5 \%, which is better than the competition baseline of 37\%.
