# An Attention Model for group-level emotion recognition

arXiv: https://arxiv.org/abs/1807.03380 · HTML: https://mlsanity.com/paper/1807.03380/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2018.

## Built on by (most cited first, 5 of 5)

- [Towards A Robust Group-level Emotion Recognition via Uncertainty-Aware Learning](https://mlsanity.com/paper/2310.04306/) (2023)
- [PERI: Part Aware Emotion Recognition In The Wild](https://mlsanity.com/paper/2210.10130/) (2022)
- [Multimodal Group Emotion Recognition In-the-wild Using Privacy-Compliant Features](https://mlsanity.com/paper/2312.05265/) (2023)
- [A Survey of Deep Learning for Group-level Emotion Recognition](https://mlsanity.com/paper/2408.15276/) (2024)
- [Variational Encoder--Multi-Decoder (VE-MD) for Privacy-by-functional-design (Group) Emotion Recognition](https://mlsanity.com/paper/2604.02397/) (2026)

## Abstract (arXiv)

In this paper we propose a new approach for classifying the global emotion of images containing groups of people. To achieve this task, we consider two different and complementary sources of information: i) a global representation of the entire image (ii) a local representation where only faces are considered. While the global representation of the image is learned with a convolutional neural network (CNN), the local representation is obtained by merging face features through an attention mechanism. The two representations are first learned independently with two separate CNN branches and then fused through concatenation in order to obtain the final group-emotion classifier. For our submission to the EmotiW 2018 group-level emotion recognition challenge, we combine several variations of the proposed model into an ensemble, obtaining a final accuracy of 64.83% on the test set and ranking 4th among all challenge participants.