Facial Emotion Recognition

## Overview

Facial Emotion Recognition is a dataset for object detection tasks - it contains Emotions annotations for 4,540 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Interactive Facial Expression and Emotion Detection (IFEED) is an annotated dataset that can be used to train, validate, and test Deep Learning models for facial expression and emotion recognition. It contains pre-filtered and analysed images of the interactions between the six main characters of the Friends television series, obtained from the video recordings of the Multimodal EmotionLines Dataset (MELD).

The images were obtained by decomposing the videos into multiple frames and extracting the facial expression of the correctly identified characters. A team composed of 14 researchers manually verified and annotated the processed data into several classes: Angry, Sad, Happy, Fearful, Disgusted, Surprised and Neutral.

IFEED can be valuable for the development of intelligent facial expression recognition solutions and emotion detection software, enabling binary or multi-class classification, or even anomaly detection or clustering tasks. The images with ambiguous or very subtle facial expressions can be repurposed for adversarial learning. The dataset can be combined with additional data recordings to create more complete and extensive datasets and improve the generalization of robust deep learning models.

Emotion Detection Model for Facial Expressions

Project Description:

In this project, we developed an Emotion Detection Model using a curated dataset of 715 facial images, aiming to accurately recognize and categorize expressions into five distinct emotion classes. The emotion classes include Happy, Sad, Fearful, Angry, and Neutral.

Objectives: - Train a robust machine learning model capable of accurately detecting and classifying facial expressions in real-time. - Implement emotion detection to enhance user experience in applications such as human-computer interaction, virtual assistants, and emotion-aware systems.

Methodology: 1. Data Collection and Preprocessing: - Assembled a diverse dataset of 715 images featuring individuals expressing different emotions. - Employed Roboflow for efficient data preprocessing, handling image augmentation and normalization.

1. Model Architecture:

   • Utilized a convolutional neural network (CNN) architecture to capture spatial hierarchies in facial features.
   • Implemented a multi-class classification approach to categorize images into the predefined emotion classes.

2. Training and Validation:

   • Split the dataset into training and validation sets for model training and evaluation.
   • Fine-tuned the model parameters to optimize accuracy and generalization.

3. Model Evaluation:

   • Evaluated the model's performance on an independent test set to assess its ability to generalize to unseen data.
   • Analyzed confusion matrices and classification reports to understand the model's strengths and areas for improvement.

4. Deployment and Integration:

   • Deployed the trained emotion detection model for real-time inference.
   • Integrated the model into applications, allowing users to interact with systems based on detected emotions.

Results: The developed Emotion Detection Model demonstrates high accuracy in recognizing and classifying facial expressions across the defined emotion classes. This project lays the foundation for integrating emotion-aware systems into various applications, fostering more intuitive and responsive interactions.

The Facial Emotion Detection Dataset is a collection of images of individuals with two different emotions - happy and sad. The dataset was captured using a mobile phone camera and contains photos taken from different angles and backgrounds.

The dataset contains a total of 637 photos with an additional dataset of 127 from previous work. Out of the total, 402 images are of happy faces, and 366 images are of sad faces. Each individual had a minimum of 10 images of both expressions.

The project faced challenges in terms of time constraints and people's constraints, which limited the number of individuals who participated. Despite the limitations, the dataset can be used for deep learning projects and real-time emotion detection models. Future work can expand the dataset by capturing more images to improve the accuracy of the model. The dataset can also be used to create a custom object detection model to evaluate other types of emotional expressions.

Here are a few use cases for this project:

1. Mental Health Monitoring: The emotion recognition model could be used in a mental health tracking app to analyze users' facial expressions during video diaries or calls, providing insights into their emotional state over time.

2. Customer Service Improvement: Businesses could use this model to monitor customer interactions in stores, analysing the facial expressions of customers to gauge their satisfaction level or immediate reaction to products or services.

3. Educational and Learning Enhancement: This model could be used in an interactive learning platform to observe students' emotional responses to different learning materials, enabling tailored educational experiences.

4. Online Content Testing: Marketing or content creation teams could utilize this model to test viewers' emotional reactions to different advertisements or content pieces, improving the impact of their messaging.

5. Social Robotics: The emotion recognition model could be incorporated in social robots or AI assistants to identify human emotions and respond accordingly, improving their overall user interaction and experience.

The Emotion Detection And Recognition Market report segments the industry into By Software And Services (Software, Services), By End-User Vertical (Government, Healthcare, Retail, Entertainment, Transportation, Other End-User Verticals), and By Geography (North America, Europe, Asia Pacific, Latin America, Middle East & Africa, Rest Of The World). Get five years of historical data alongside five-year market forecasts.

Video Dataset of Various Emotions for Recognition Tasks

Dataset comprises 1,000+ videos featuring 11 facial emotions and 15 inner emotions expressed by individuals from diverse backgrounds, including various races, genders, and ages. It is designed for emotion recognition research, focusing on emotion detection and emotion classification tasks. By utilizing this dataset, researchers can explore advanced emotion analysis techniques and develop robust recognition models that can… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/video-emotion-recognition-dataset.

Computer Vision Scientist-Collected Dataset:This facial emotion recognition dataset has been meticulously curated by computer vision scientists using mobile phone cameras to capture candid moments of individuals expressing a spectrum of emotions, including Happy, Sad, Fear, and Humor. The dataset comprises a rich collection of images with diverse angles and backgrounds, providing a realistic portrayal of human emotional expression.Marketing Expert-Collected Dataset:The ethnicity-focused dataset for facial recognition has been meticulously assembled by marketing experts, aiming to shed light on the vital aspect of ethnicity variations in computer vision. With a dedicated focus on ethnicity, this dataset provides a unique perspective for training and testing facial recognition models in an ethnically diverse context.This dataset comprises a rich collection of images capturing individuals from various ethnic backgrounds. It emphasises the importance of ethnicity in the field of computer vision and includes a wide range of facial features, expressions, and poses, thereby enriching the dataset's diversity.By offering insights into the critical area of ethnicity in computer vision, this dataset is a valuable addition to the toolkit of researchers and practitioners, facilitating the development of more inclusive and accurate facial recognition models.Researchers and experts in the fields of computer vision and marketing are encouraged to explore these datasets for their research, model development, and the advancement of understanding in these respective domains.

Emotion recognition Dataset

Dataset comprises 199,955 images featuring 28,565 individuals displaying a variety of facial expressions. It is designed for research in emotion recognition and facial expression analysis across diverse races, genders, and ages.

By utilizing this dataset, researchers and developers can enhance their understanding of facial recognition technology and improve the accuracy of emotion classification systems. - Get the data

Examples of data

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F22472a4de7d505ff4962b7eaa14071bf%2F1.png?generation=1740432470830146&alt=media" alt="">

This dataset includes images that capture different emotions, such as happiness, sadness, surprise, anger, disgust, and fear, allowing researchers to develop and evaluate recognition algorithms and detection methods.

💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.

Metadata for the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F8cfad327bf19d7f6fad22ae2cc021a5b%2FFrame%201%20(2).png?generation=1740432926933026&alt=media" alt=""> Researchers can leverage this dataset to explore various learning methods and algorithms aimed at improving emotion detection and facial expression recognition.

🌐 UniData provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects

Introduction

Welcome to the South Asian Facial Expression Image Dataset, meticulously curated to enhance expression recognition models and support the development of advanced biometric identification systems, KYC models, and other facial recognition technologies.

Facial Expression Data

This dataset comprises over 2000 facial expression images, divided into participant-wise sets with each set including:

Diversity and Representation

The dataset includes contributions from a diverse network of individuals across South Asian countries, such as:

Quality and Conditions

To ensure high utility and robustness, all images are captured under varying conditions:

Metadata

Each facial expression image set is accompanied by detailed metadata for each participant, including:

This metadata is essential for training models that can accurately recognize and identify expressions across different demographics and conditions.

Usage and Applications

This facial emotion dataset is ideal for various applications in the field of computer vision, including but not limited to:

Secure and Ethical Collection

Updates and Customization

We understand the evolving nature of AI and machine learning requirements. Therefore, we continuously add more assets with diverse conditions to this off-the-shelf facial expression dataset.

Human Emotion Detection

## Overview

Human Emotion Detection is a dataset for classification tasks - it contains Emotions annotations for 981 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Emotion Detection and Recognition Market size is expected to reach a valuation of USD 75502.4 million in 2033 growing at a CAGR of 13.50%. The research report classifies market by share, trend, demand and based on segmentation by Component, Software, Application, End Users, Verticals and Regiona...

40 sub-folders are further divided in this directory, each sub-folder contains the data of all the facial expression per participant. The sub-folders are named after the participant ID and include 4 sub-sub folders which are central RGB (CRGB) facial expression data, left RGB (LRGB) facial expression data, right RGB (RRGB) facial expression data, and central infrared (CIR) facial expression data. Each folder contains multiple MP4 files, and each MP4 file corresponds to valid emotional driving.

Emotion Detection and Recognition Market Outlook

The global Emotion Detection and Recognition market size is projected to grow from an estimated USD 23.5 billion in 2023 to USD 61.3 billion by 2032, reflecting a robust Compound Annual Growth Rate (CAGR) of 11.2% during the forecast period. This impressive growth is driven by increasing adoption of Artificial Intelligence (AI) technologies and machine learning algorithms that enhance the capability of emotion detection systems across various industries. The demand for such systems is particularly fueled by the need for improved customer experience, advanced human-machine interactions, and the growing proliferation of wearable devices that can capture and analyze emotional data seamlessly.

One of the major growth factors in the Emotion Detection and Recognition market is the escalating demand for enhanced customer experience across diverse sectors. Businesses are increasingly investing in emotion recognition technologies to better understand customer emotions, preferences, and behaviors. This enables companies to personalize their services and interactions, significantly improving customer satisfaction and loyalty. Moreover, with the rapid advancements in AI and machine learning, these technologies have become more sophisticated, offering higher accuracy and real-time processing capabilities that are crucial for applications in retail, customer service, and marketing sectors.

Another significant growth driver is the burgeoning use of emotion recognition technology in healthcare applications. This technology aids in monitoring patient emotions, which can be pivotal in diagnosing mental health conditions and tailoring personalized treatment plans. The ability to accurately detect and analyze emotions can enhance therapeutic interventions and patient management, thereby improving healthcare outcomes. Additionally, the integration of biosensing technologies in wearable health devices further propels the market, providing real-time emotional data that can be used for continuous health monitoring and early diagnosis of psychiatric conditions.

Furthermore, the rising trend of smart vehicles and autonomous driving technologies is expected to significantly bolster the market growth. Emotion detection systems are being integrated into vehicles to enhance driver safety and comfort. These systems can monitor driver fatigue, stress levels, and overall emotional state, alerting them or the vehicle system to take necessary actions to prevent accidents. As automotive manufacturers continue to innovate and incorporate AI-driven safety features, the demand for emotion recognition technologies is anticipated to rise sharply, contributing to the market's expansion.

Affective Computing Solutions are increasingly becoming integral to the advancement of emotion detection and recognition technologies. These solutions involve the development of systems and devices that can recognize, interpret, and process human emotions. By leveraging affective computing, businesses can enhance their customer interaction strategies, providing more personalized and empathetic experiences. This technology is particularly beneficial in sectors such as healthcare, where understanding patient emotions can lead to better diagnosis and treatment outcomes. Moreover, affective computing solutions are being integrated into educational tools to create adaptive learning environments that respond to the emotional states of students, thereby improving engagement and learning efficiency.

Technology Analysis

The Emotion Detection and Recognition market is segmented by technology into facial recognition, speech recognition, biosensing, machine learning, and others. Facial recognition technology dominates the market due to its widespread applicability and effectiveness in accurately interpreting human expressions. The technology employs advanced algorithms to analyze facial features and expressions to detect emotions. Its adoption is particularly notable in sectors like retail and security, where understanding customer or personnel emotions can yield significant insights. Continuous improvements in camera systems and image processing technologies have also enhanced the precision and reliability of facial recognition systems, making them indispensable tools for emotion detection.

Speech recognition technology is another critical segment driving the Emotion Detection and Recognition market. This technology analyzes vocal tones, pitch, an

Global Emotion Detection And Recognition market size is expected to reach $58.73 billion by 2029 at 16.9%, segmented as by software tool, facial expression and emotion recognition, gesture and posture recognition, voice recognition

PME4 is a posed multimodal emotion dataset with four modalities (PME4): audio, video, EEG, and EMG. Data were collected from 11 human subjects (five female and six male individuals) who were students in acting after informed consent was obtained. This dataset consists of recognizing the six basic human emotions (anger, fear, disgust, sadness, happiness, and surprise) plus a neutral emotion for a total of seven emotions.

For more dataset details, please check out: https://github.com/jinchen1036/PME4-Emotion-Recognition/blob/main/PME4_dataset.md

Dataset Card for "emotion"

  Dataset Summary

Emotion is a dataset of English Twitter messages with six basic emotions: anger, fear, joy, love, sadness, and surprise. For more detailed information please refer to the paper.

  Supported Tasks and Leaderboards

More Information Needed

  Languages

More Information Needed

  Dataset Structure





  Data Instances

An example looks as follows. { "text": "im feeling quite sad and sorry for myself but… See the full description on the dataset page: https://huggingface.co/datasets/dair-ai/emotion.

Emotion Detection and Recognition System Market Outlook

In 2023, the global emotion detection and recognition system market size was valued at approximately USD 19.5 billion and is projected to reach USD 70.1 billion by 2032, growing at a robust CAGR of 15.3% during the forecast period. This significant growth is largely attributed to the increasing integration of artificial intelligence (AI) and machine learning technologies in various industries, which enhances the capabilities of emotion detection systems. The demand for more personalized experiences and the need for customer satisfaction across sectors have propelled the adoption of these systems. Businesses, aiming to better understand consumer behavior, are investing heavily in emotion recognition technology, further driving market expansion.

The burgeoning demand for emotion detection and recognition systems is fueled by the technological advancements in AI and machine learning, which significantly enhance the accuracy and efficiency of these systems. As organizations increasingly prioritize customer interaction and satisfaction, these systems allow businesses to gather invaluable insights into customer emotions and sentiments. This transition reflects a shift towards more emotionally intelligent systems capable of understanding and responding to human emotions. Additionally, the increasing adoption of wearable technology, equipped with biosensors, has further extended the application of emotion detection systems, as these devices provide real-time and continuous emotion monitoring, thus broadening their usage across different verticals.

Another critical growth factor for the emotion detection and recognition system market is the rising demand for advanced security solutions. With the escalation of privacy concerns and the need for safeguarded environments, emotion detection systems have found significant applications in sectors such as security and surveillance. Facial recognition and speech analysis technologies, integral components of emotion detection systems, are increasingly being utilized for identifying potential threats, thus enhancing security protocols. Moreover, government initiatives to integrate these advanced technologies into public safety measures are providing an additional boost to market growth. This trend is particularly prominent in regions with high investments in smart city projects, where such technologies are being deployed to ensure more secure and responsive urban environments.

The healthcare sector also presents a substantial growth avenue for emotion detection and recognition systems. The application of these systems in monitoring patient moods and mental health conditions is gaining prominence, as healthcare providers seek to enhance patient care through innovative technological solutions. Emotion detection systems can aid in early diagnosis and treatment by accurately interpreting patient emotions, thereby improving therapeutic outcomes. Furthermore, the integration of these systems in telehealth platforms is opening new avenues for remote patient monitoring, making healthcare more accessible and efficient.

Emotion Analytics is becoming an integral part of the emotion detection and recognition system market. By leveraging advanced data analysis techniques, Emotion Analytics provides deeper insights into human emotions, enabling businesses to make informed decisions based on emotional data. This technology is particularly valuable in sectors such as marketing and customer service, where understanding consumer emotions can lead to more personalized and effective strategies. As the demand for emotionally intelligent systems grows, Emotion Analytics is poised to play a crucial role in enhancing the capabilities of emotion detection technologies, driving further innovation and market expansion.

Regionally, North America holds a dominant position in the emotion detection and recognition system market, driven by technological advancements and the presence of key market players. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, fueled by the rapid digital transformation and increasing investments in AI technologies in countries like China and India. The region's expanding consumer base and growing adoption of smart devices further contribute to this growth trend. Meanwhile, Europe continues to showcase a steady demand for emotion detection systems, driven by advancements in automotive and healthcare sectors, and an increasing focus on enhancing customer experience across industries. The Middle East &am

pt-sk/Face-Emotion-Detection dataset hosted on Hugging Face and contributed by the HF Datasets community

Emotion Detection and Recognition Market size was valued at USD 95.41 Billion in 2024 and is projected to reach USD 38.26 Billion by 2032, growing at a CAGR of 12.1% during the forecast period 2026-2032.

Global Emotion Detection And Recognition Market Drivers

The market drivers for the Emotion Detection And Recognition Market can be influenced by various factors. These may include:

Growing need: One major market driver is the rising need for sophisticated emotion detection and recognition technologies across a range of industries, including healthcare, retail, and entertainment.

Technological Developments: The accuracy and efficiency of emotion detection and recognition systems have been greatly improved by the quick developments in artificial intelligence (AI), machine learning (ML), and deep learning algorithms.

Growing Awareness: The adoption of emotion detection and recognition solutions is being driven by enterprises' growing realization of how important it is to understand consumer emotions in order to improve customer happiness, user experience, and decision-making processes. Need for Personalization: Companies are using emotion detection and recognition technologies more and more to tailor their goods and services to the unique feelings, tastes, and actions of each client.

Enhanced Security Measures: To detect suspicious behavior, stop crime, and improve overall security measures, emotion detection and recognition systems are being used for security purposes in a number of industries.

Government Initiatives: The industry is expanding as a result of laws and policies that encourage the use of cutting-edge technology, such as those that detect and identify emotions.

Integration with Internet of Things Devices: The ability to detect and recognize emotions in conjunction with Internet of Things (IoT) devices is creating new possibilities for wearable technology, smart home applications, and healthcare monitoring.

Trends in Remote Work: The COVID-19 pandemic and other reasons have increased the trend of remote work and virtual communication, which has increased demand for emotion detection and recognition technology to improve virtual collaboration, communication, and employee well-being.

Growth of the Entertainment sector: Applications such as virtual reality experiences, audience engagement analysis, and content customisation are causing the entertainment sector to employ emotion detection and recognition technologies more and more.

Accessibility and Affordability: Technological developments have lowered the cost of emotion detection and recognition systems for companies of all sizes, promoting their widespread use in a variety of industries.