  Facial expressions have essential cues to infer the humans state of mind,
that conveys adequate information to understand individuals actual feelings.
Thus, automatic facial expression recognition is an interesting and crucial
task to interpret the humans cognitive state through the machine. In this
paper, we proposed an Exigent Features Preservative Network (EXPERTNet), to
describe the features of the facial expressions. The EXPERTNet extracts only
pertinent features and neglect others by using exigent feature (ExFeat) block,
mainly comprises of elective layer. Specifically, elective layer selects the
desired edge variation features from the previous layer outcomes, which are
generated by applying different sized filters as 1 x 1, 3 x 3, 5 x 5 and 7 x 7.
Different sized filters aid to elicits both micro and high-level features that
enhance the learnability of neurons. ExFeat block preserves the spatial
structural information of the facial expression, which allows to discriminate
between different classes of facial expressions. Visual representation of the
proposed method over different facial expressions shows the learning capability
of the neurons of different layers. Experimental and comparative analysis
results over four comprehensive datasets CK+, MMI DISFA and GEMEP-FERA, ensures
the better performance of the proposed network as compared to existing
networks.
