# Context Based Emotion Recognition using EMOTIC Dataset

arXiv: https://arxiv.org/abs/2003.13401 · HTML: https://mlsanity.com/paper/2003.13401/

## Key facts

- Cited by 73 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2020.

## Built on by (most cited first, 30 of 73)

- [Affective Image Content Analysis: Two Decades Review and New Perspectives](https://mlsanity.com/paper/2106.16125/) (2021)
- [Context De-confounded Emotion Recognition](https://mlsanity.com/paper/2303.11921/) (2023)
- [Robust Emotion Recognition in Context Debiasing](https://mlsanity.com/paper/2403.05963/) (2024)
- [FindingEmo: An Image Dataset for Emotion Recognition in the Wild](https://mlsanity.com/paper/2402.01355/) (2024)
- [Global-Local Attention for Emotion Recognition](https://mlsanity.com/paper/2111.04129/) (2021)
- [Graph-based Facial Affect Analysis: A Review](https://mlsanity.com/paper/2103.15599/) (2021)
- [Leveraging Semantic Scene Characteristics and Multi-Stream Convolutional Architectures in a Contextual Approach for Video-Based Visual Emotion Recognition in the Wild](https://mlsanity.com/paper/2105.07484/) (2021)
- [An audiovisual and contextual approach for categorical and continuous emotion recognition in-the-wild](https://mlsanity.com/paper/2107.03465/) (2021)
- [Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion](https://mlsanity.com/paper/2307.13463/) (2023)
- [VEATIC: Video-based Emotion and Affect Tracking in Context Dataset](https://mlsanity.com/paper/2309.06745/) (2023)
- [Large Vision-Language Models as Emotion Recognizers in Context Awareness](https://mlsanity.com/paper/2407.11300/) (2024)
- [Towards Context-Aware Emotion Recognition Debiasing from a Causal Demystification Perspective via De-confounded Training](https://mlsanity.com/paper/2407.04963/) (2024)
- [Learning to Compose Diversified Prompts for Image Emotion Classification](https://mlsanity.com/paper/2201.10963/) (2022)
- [Contextual Emotion Recognition using Large Vision Language Models](https://mlsanity.com/paper/2405.08992/) (2024)
- [StimuVAR: Spatiotemporal Stimuli-aware Video Affective Reasoning with Multimodal Large Language Models](https://mlsanity.com/paper/2409.00304/) (2024)
- [High-Level Context Representation for Emotion Recognition in Images](https://mlsanity.com/paper/2305.03500/) (2023)
- [Computational Emotion Analysis From Images: Recent Advances and Future Directions](https://mlsanity.com/paper/2103.10798/) (2021)
- [Predicting emotion from music videos: exploring the relative contribution of visual and auditory information to affective responses](https://mlsanity.com/paper/2202.10453/) (2022)
- [CAGE: Circumplex Affect Guided Expression Inference](https://mlsanity.com/paper/2404.14975/) (2024)
- [Identifying the Context Shift between Test Benchmarks and Production Data](https://mlsanity.com/paper/2207.01059/) (2022)
- [On the use of Vision-Language models for Visual Sentiment Analysis: a study on CLIP](https://mlsanity.com/paper/2310.12062/) (2023)
- [Two in One Go: Single-stage Emotion Recognition with Decoupled Subject-context Transformer](https://mlsanity.com/paper/2404.17205/) (2024)
- [Multiverse Through Deepfakes: The MultiFakeVerse Dataset of Person-Centric Visual and Conceptual Manipulations](https://mlsanity.com/paper/2506.00868/) (2025)
- [EmoFeedback2: Reinforcement of Continuous Emotional Image Generation via LVLM-based Reward and Textual Feedback](https://mlsanity.com/paper/2511.19982/) (2025)
- [Estimating Structural Disparities for Face Models](https://mlsanity.com/paper/2204.06562/) (2022)
- [Multi-Task Multi-Modal Self-Supervised Learning for Facial Expression Recognition](https://mlsanity.com/paper/2404.10904/) (2024)
- [MMAFFBen: A Multilingual and Multimodal Affective Analysis Benchmark for Evaluating LLMs and VLMs](https://mlsanity.com/paper/2505.24423/) (2025)
- [VIBE: Can a VLM Read the Room?](https://mlsanity.com/paper/2506.11162/) (2025)
- [PERI: Part Aware Emotion Recognition In The Wild](https://mlsanity.com/paper/2210.10130/) (2022)
- [EMERSK -- Explainable Multimodal Emotion Recognition with Situational Knowledge](https://mlsanity.com/paper/2306.08657/) (2023)

## Abstract (arXiv)

In our everyday lives and social interactions we often try to perceive the emotional states of people. There has been a lot of research in providing machines with a similar capacity of recognizing emotions. From a computer vision perspective, most of the previous efforts have been focusing in analyzing the facial expressions and, in some cases, also the body pose. Some of these methods work remarkably well in specific settings. However, their performance is limited in natural, unconstrained environments. Psychological studies show that the scene context, in addition to facial expression and body pose, provides important information to our perception of people's emotions. However, the processing of the context for automatic emotion recognition has not been explored in depth, partly due to the lack of proper data. In this paper we present EMOTIC, a dataset of images of people in a diverse set of natural situations, annotated with their apparent emotion. The EMOTIC dataset combines two different types of emotion representation: (1) a set of 26 discrete categories, and (2) the continuous dimensions Valence, Arousal, and Dominance. We also present a detailed statistical and algorithmic analysis of the dataset along with annotators' agreement analysis. Using the EMOTIC dataset we train different CNN models for emotion recognition, combining the information of the bounding box containing the person with the contextual information extracted from the scene. Our results show how scene context provides important information to automatically recognize emotional states and motivate further research in this direction. Dataset and code is open-sourced and available at: https://github.com/rkosti/emotic and link for the peer-reviewed published article: https://ieeexplore.ieee.org/document/8713881