# RARITYNet: Rarity Guided Affective Emotion Learning Framework

arXiv: https://arxiv.org/abs/2205.08595 · HTML: https://mlsanity.com/paper/2205.08595/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 17 May 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Going Deeper in Facial Expression Recognition using Deep Neural Networks](https://mlsanity.com/paper/1511.04110/) (2015)
- [FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition](https://mlsanity.com/paper/1609.06591/) (2016)
- [Do Deep Neural Networks Learn Facial Action Units When Doing Expression Recognition?](https://mlsanity.com/paper/1510.02969/) (2015)
- [Point Adversarial Self Mining: A Simple Method for Facial Expression Recognition](https://mlsanity.com/paper/2008.11401/) (2020)

## Abstract (arXiv)

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.