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.

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The dataset comprises 16.7k images and 2 annotation files, each in a distinct format. The first file labeled Label contains annotations with the original scale.

The Real-world Affective Faces Database (RAF-DB) is a dataset for facial expression. This version Contains 15000k facial images tagged with basic or compound expressions by 40 independent taggers. Images in this database are of great variability in subjects' age, gender and ethnicity, head poses, lighting conditions, occlusions, (e.g. glasses, facial hair or self-occlusion), post-processing operations (e.g. various filters and special effects), etc.

For More Info Visit: Here

Terms & Conditions

The RAF database is available for non-commercial research purposes only.

All images of the RAF database are obtained from the Internet which are not property of PRIS, Beijing University of Posts and Telecommunications. The PRIS is not responsible for the content nor the meaning of these images.

You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.

You agree not to further copy, publish or distribute any portion of the RAF database. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset.

The PRIS reserves the right to terminate your access to the RAF database at any time.

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.

V.1 is deprecated, use V.2 instead.

The images are the same: only the README file has been updated.

https://doi.org/10.5281/zenodo.14974867

The JAFFE images may be used only for non-commercial scientific research.

The source and background of the dataset must be acknowledged by citing the following two articles. Users should read both carefully.

Michael J. Lyons, Miyuki Kamachi, Jiro Gyoba.
Coding Facial Expressions with Gabor Wavelets (IVC Special Issue)
arXiv:2009.05938 (2020) https://arxiv.org/pdf/2009.05938.pdf

Michael J. Lyons
"Excavating AI" Re-excavated: Debunking a Fallacious Account of the JAFFE Dataset
arXiv: 2107.13998 (2021) https://arxiv.org/abs/2107.13998

The following is not allowed:

• Redistribution of the JAFFE dataset (incl. via Github, Kaggle, Colaboratory, GitCafe, CSDN etc.)
• Posting JAFFE images on the web and social media
• Public exhibition of JAFFE images in museums/galleries etc.
• Broadcast in the mass media (tv shows, films, etc.)

A few sample images (not more than 10) may be displayed in scientific publications.

Dataset

This dataset was created by Utkarsh Mathur

Released under ODC Attribution License (ODC-By)

Contents

Face Mask Detection Kaggle

## Overview

Face Mask Detection Kaggle is a dataset for object detection tasks - it contains Masks annotations for 848 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).

This dataset is a modified version of the dataset "Facial Emotion Expressions" available here -> dataset link.
This modified version handles the problem of class imbalance by adding augmented images of class "disgust" (original amount : 432, new amount : 4300+) and reducing the number of images in class happy(original amount : 7300+, new amount : 4500).
This modification was done to try and address the problem of class imbalance. Peace out 😊✌️.

The JAFFE images may be used only for non-commercial scientific research.

The source and background of the dataset must be acknowledged by citing the following two articles. Users should read both carefully.

Michael J. Lyons, Miyuki Kamachi, Jiro Gyoba.
Coding Facial Expressions with Gabor Wavelets (IVC Special Issue)
arXiv:2009.05938 (2020) https://arxiv.org/pdf/2009.05938.pdf

Michael J. Lyons
"Excavating AI" Re-excavated: Debunking a Fallacious Account of the JAFFE Dataset
arXiv: 2107.13998 (2021) https://arxiv.org/abs/2107.13998

The following is not allowed:

• Redistribution of the JAFFE dataset (incl. via Github, Kaggle, Colaboratory, GitCafe, CSDN etc.)
• Posting JAFFE images on the web and social media
• Public exhibition of JAFFE images in museums/galleries etc.
• Broadcast in the mass media (tv shows, films, etc.)

A few sample images (not more than 10) may be displayed in scientific publications.

4,458 People - 3D Facial Expressions Recognition Data. The collection scenes include indoor scenes and outdoor scenes. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes different expressions, different ages, different races, different collecting scenes. This data can be used for tasks such as 3D facial expression recognition.

Data Description

The Expression in-the-Wild (ExpW) Dataset is a comprehensive and diverse collection of facial images carefully curated to capture spontaneous and unscripted facial expressions exhibited by individuals in real-world scenarios. This extensively annotated dataset serves as a valuable resource for advancing research in the fields of computer vision, facial expression analysis, affective computing, and human behavior understanding.

Key Features:

1. Real-world Expressions: The ExpW dataset stands apart from traditional lab-controlled datasets as it focuses on capturing facial expressions in real-life environments. This authenticity ensures that the dataset reflects the natural diversity of emotions experienced by individuals in everyday situations, making it highly relevant for real-world applications.

2. Large and Diverse: Comprising a vast number of images, the ExpW dataset encompasses an extensive range of subjects, ethnicities, ages, and genders. This diversity allows researchers and developers to build more robust and inclusive models for facial expression recognition and emotion analysis.

3. Annotated Emotions: Each facial image in the dataset is meticulously annotated with corresponding emotion labels, including but not limited to happiness, sadness, anger, surprise, fear, disgust, and neutral expressions. The emotion annotations provide ground truth data for training and validating machine learning algorithms.

4. Various Pose and Illumination: To account for the varying challenges posed by real-life scenarios, the ExpW dataset includes images captured under different lighting conditions and poses. This variability helps researchers create algorithms that are robust to changes in illumination and head orientation.

5. Privacy and Ethics: ExpW has been compiled adhering to strict privacy and ethical guidelines, ensuring the subjects' consent and data protection. The dataset maintains a high level of anonymity by excluding any personal information or sensitive details.

This dataset has been downloaded from the following Public Directory... https://drive.google.com/drive/folders/1SDcI273EPKzzZCPSfYQs4alqjL01Kybq

Dataset contains 91,793 faces manually labeled with expressions (Figure 1). Each of the face images is annotated as one of the seven basic expression categories: “angry (0)”, “disgust (1)”, “fear (2)”, “happy (3)”, “sad (4)”, “surprise (5)”, or “neutral (6)”.

Dataset Summary

A dataset from kaggle. origin: https://dphi.tech/challenges/data-sprint-76-human-activity-recognition/233/data

  Introduction

• PROBLEM STATEMENT
  

• About Files
  

Train - contains all the images that are to be used for training your model. In this folder you will find 15 folders namely - 'calling', ’clapping’, ’cycling’, ’dancing’, ‘drinking’, ‘eating’, ‘fighting’, ‘hugging’, ‘laughing’, ‘listeningtomusic’, ‘running’, ‘sitting’… See the full description on the dataset page: https://huggingface.co/datasets/Kai1014/facemask-kaggle.

From the site: Masks play a crucial role in protecting the health of individuals against respiratory diseases, as is one of the few precautions available for COVID-19 in the absence of immunization. With this dataset, it is possible to create a model to detect people wearing masks, not wearing them, or wearing masks improperly. This dataset contains 853 images belonging to the 3 classes, as well as their bounding boxes in the PASCAL VOC format. The classes are:

With mask; Without mask; Mask worn incorrectly.

The NAVARASA dataset comprises images representing nine basic facial emotions: anger, disgust, fear, happiness, sadness, surprise, calmness, love, and peace. Each emotion category contains images depicting individuals displaying the corresponding emotion through facial expressions. This dataset serves as a valuable resource for training and evaluating facial emotion recognition systems, contributing to advancements in computer vision and affective computing research.

This large-scale face image dataset features 10,109 individuals from various countries and ethnic backgrounds. Each subject has been captured in multiple real-world scenarios, resulting in diverse facial images under varying angles, lighting conditions, and expressions. Detailed annotations include gender, race, and age, making the dataset suitable for tasks such as facial recognition, face clustering, demographic analysis, and machine learning model training.The dataset has been validated by multiple AI companies and proven to deliver strong performance in real-world applications. All data collection, storage, and processing strictly adhere to global data protection regulations, including GDPR, CCPA, and PIPL, ensuring legal compliance and privacy preservation.

Dataset

This dataset was created by Abhishek Panigrahi

Contents

Original datasets https://www.kaggle.com/datasets/ashishjangra27/face-mask-12k-images-dataset

Gender Detection & Classification - face recognition dataset

The dataset is created on the basis of Face Mask Detection dataset

Dataset Description:

The dataset comprises a collection of photos of people, organized into folders labeled "women" and "men." Each folder contains a significant number of images to facilitate training and testing of gender detection algorithms or models.

The dataset contains a variety of images capturing female and male individuals from diverse backgrounds, age groups, and ethnicities.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F1c4708f0b856f7889e3c0eea434fe8e2%2FFrame%2045%20(1).png?generation=1698764294000412&alt=media" alt="">

This labeled dataset can be utilized as training data for machine learning models, computer vision applications, and gender detection algorithms.

💴 For Commercial Usage: Full version of the dataset includes 376 000+ photos of people, leave a request on TrainingData to buy the dataset

Metadata for the full dataset:

• assignment_id - unique identifier of the media file
• worker_id - unique identifier of the person
• age - age of the person
• true_gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• photo_1_extension, photo_2_extension, photo_3_extension, photo_4_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, photo_3_extension, photo_4_resolution - photo resolution in the dataset

OTHER BIOMETRIC DATASETS:

• Anti Spoofing Real Dataset
• Antispoofing Replay Dataset
• Selfies, ID Images dataset (5591 sets of 15 files)
• Selfies and video dataset (4 052 sets)
• Dataset of bald people, 5000 images

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to learn about the price and buy the dataset

Content

The dataset is split into train and test folders, each folder includes: - folders women and men - folders with images of people with the corresponding gender, - .csv file - contains information about the images and people in the dataset

File with the extension .csv

• file: link to access the file,
• gender: gender of a person in the photo (woman/man),
• split: classification on train and test

TrainingData provides high-quality data annotation tailored to your needs

keywords: biometric system, biometric system attacks, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, gender detection, supervised learning dataset, gender classification dataset, gender recognition dataset

The image source of this dataset is from https://www.kaggle.com/datasets/andrewmvd/face-mask-detection .

This Human Face Segmentation Dataset contains 70,846 high-quality images featuring diverse subjects with pixel-level annotations. The dataset includes individuals across various age groups—from young children to the elderly—and represents multiple ethnicities, including Asian, Black, and Caucasian. Both males and females are included. The scenes range from indoor to outdoor environments, with pure-color backgrounds also present. Facial expressions vary from neutral to complex, including large-angle head tilts, eye closures, glowers, puckers, open mouths, and more. Each image is precisely annotated on a pixel-by-pixel basis, covering facial regions, five sense organs, body parts, and appendages. This dataset is ideal for applications such as facial recognition, segmentation, and other computer vision tasks involving human face parsing.