  Inspired from the assets of handcrafted and deep learning approaches, we
proposed a RARITYNet: RARITY guided affective emotion learning framework to
learn the appearance features and identify the emotion class of facial
expressions. The RARITYNet framework is designed by combining the shallow
(RARITY) and deep (AffEmoNet) features to recognize the facial expressions from
challenging images as spontaneous expressions, pose variations, ethnicity
changes, and illumination conditions. The RARITY is proposed to encode the
inter-radial transitional patterns in the local neighbourhood. The AffEmoNet:
affective emotion learning network is proposed by incorporating three feature
streams: high boost edge filtering (HBSEF) stream, to extract the edge
information of highly affected facial expressive regions, multi-scale
sophisticated edge cumulative (MSSEC) stream is to learns the sophisticated
edge information from multi-receptive fields and RARITY uplift complementary
context feature (RUCCF) stream refines the RARITY-encoded features and aid the
MSSEC stream features to enrich the learning ability of RARITYNet.
