IMDb-Face is large-scale noise-controlled dataset for face recognition research. The dataset contains about 1.7 million faces, 59k identities, which is manually cleaned from 2.0 million raw images. All images are obtained from the IMDb website.

Image Dataset of face images for compuer vision tasks

Dataset comprises 500,600+ images of individuals representing various races, genders, and ages, with each person having a single face image. It is designed for facial recognition and face detection research, supporting the development of advanced recognition systems. By leveraging this dataset, researchers and developers can enhance deep learning models, improve face verification and face identification techniques, and refine… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/face-recognition-image-dataset.

Expressions

Introduction

Welcome to the Caucasian Human Facial Images Dataset, meticulously curated to enhance face recognition models and support the development of advanced biometric identification systems, KYC models, and other facial recognition technologies.

Facial Image Data

This dataset comprises over 1,000 Caucasian individual facial image sets, with each set including:

Diversity and Representation

The dataset includes contributions from a diverse network of individuals across Caucasian countries.

Quality and Conditions

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

Metadata

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

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

Usage and Applications

This facial image 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 image dataset.

Similar face recognition has always been one of the most challenging research directions in face recognition.This project shared similar face images (SFD.zip) that we have collected so far. All images are labeld and collected from publicly available datasets such as LFW, CASIA-WebFace.We will continue to collect larger-scale data and continue to update this project.Because the data set is too large, we uploaded a compressed zip file (SFD.zip). Meanwhile here we upload a few examples for everyone to view.email: ileven@shu.edu.cn

Metfaces Image Dataset

Metropolitan Museum of Art Faces Image Dataset

By huggan (From Huggingface) [source]

About this dataset

Researchers and developers can leverage this dataset to explore and analyze facial representations depicted in different artistic styles throughout history. These images represent a rich tapestry of human expressions, cultural diversity, and artistic interpretations, providing ample opportunities for leveraging computer vision techniques.

By utilizing this extensive dataset during model training, machine learning practitioners can enhance their algorithms' ability to recognize and interpret facial elements accurately. This is particularly beneficial in applications such as face recognition systems, emotion detection algorithms, portrait analysis tools, or even historical research endeavors focusing on portraiture.

How to use the dataset

• Downloading the Dataset:

  Start by downloading the dataset from Kaggle's website. The dataset file is named train.csv, which contains the necessary image data for training your models.

• Exploring the Data:

  Once you have downloaded and extracted the dataset, it's time to explore its contents. Load the train.csv file into your preferred programming environment or data analysis tool to get an overview of its structure and columns.

• Understanding the Columns:

  The main column of interest in this dataset is called image. This column contains links or references to specific images in the Metropolitan Museum of Art's collection, showcasing different faces captured within them.

• Accessing Images from URLs or References:

  To access each image associated with their respective URLs or references, you can write code or use libraries that support web scraping or download functionality. Each row under the image column will provide you with a URL or reference that can be used to fetch and download that particular image.

• Preprocessing and Data Augmentation (Optional):

  Depending on your use case, you might need to perform various preprocessing techniques on these images before using them as input for your machine learning models. Preprocessing steps may include resizing, cropping, normalization, color space conversions, etc.

• Training Machine Learning Models:

  Once you have preprocessed any necessary data, it's time to start training your machine learning models using this image dataset as training samples.

• Analysis and Evaluation:

  After successfully training your model(s), evaluate their performance using validation datasetse if available . You can also make predictions on unseen images, measure accuracy, and analyze the results to gain insights or adjust your models accordingly.

• Additional Considerations:

  Remember to give appropriate credit to the Metropolitan Museum of Art for providing this image dataset when using it in research papers or other publications. Additionally, be aware of any licensing restrictions or terms of use associated with the images themselves.

Research Ideas

• Facial recognition: This dataset can be used to train machine learning models for facial recognition systems. By using the various images of faces from the Metropolitan Museum of Art, the models can learn to identify and differentiate between different individuals based on their facial features.
• Emotion detection: The images in this dataset can be utilized for training models that can detect emotions on human faces. This could be valuable in applications such as market research, where understanding customer emotional responses to products or advertisements is crucial.
• Cultural analysis: With a diverse range of historical faces from different times and regions, this dataset could be employed for cultural analysis and exploration. Machine learning algorithms can identify common visual patterns or differences among different cultures, shedding light on the evolution of human appearances across time and geography

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv | Column name | Description ...

The dataset for this project is characterised by photos of individual human emotion expression and these photos are taken with the help of both digital camera and a mobile phone camera from different angles, posture, background, light exposure, and distances. This task might look and sound very easy but there were some challenges encountered along the process which are reviewed below: 1) People constraint One of the major challenges faced during this project is getting people to participate in the image capturing process as school was on vacation, and other individuals gotten around the environment were not willing to let their images be captured for personal and security reasons even after explaining the notion behind the project which is mainly for academic research purposes. Due to this challenge, we resorted to capturing the images of the researcher and just a few other willing individuals. 2) Time constraint As with all deep learning projects, the more data available the more accuracy and less error the result will produce. At the initial stage of the project, it was agreed to have 10 emotional expression photos each of at least 50 persons and we can increase the number of photos for more accurate results but due to the constraint in time of this project an agreement was later made to just capture the researcher and a few other people that are willing and available. These photos were taken for just two types of human emotion expression that is, “happy” and “sad” faces due to time constraint too. To expand our work further on this project (as future works and recommendations), photos of other facial expression such as anger, contempt, disgust, fright, and surprise can be included if time permits. 3) The approved facial emotions capture. It was agreed to capture as many angles and posture of just two facial emotions for this project with at least 10 images emotional expression per individual, but due to time and people constraints few persons were captured with as many postures as possible for this project which is stated below: Ø Happy faces: 65 images Ø Sad faces: 62 images There are many other types of facial emotions and again to expand our project in the future, we can include all the other types of the facial emotions if time permits, and people are readily available. 4) Expand Further. This project can be improved furthermore with so many abilities, again due to the limitation of time given to this project, these improvements can be implemented later as future works. In simple words, this project is to detect/predict real-time human emotion which involves creating a model that can detect the percentage confidence of any happy or sad facial image. The higher the percentage confidence the more accurate the facial fed into the model. 5) Other Questions Can the model be reproducible? the supposed response to this question should be YES. If and only if the model will be fed with the proper data (images) such as images of other types of emotional expression.

This dataset has been used in this paper: Face Clustering for Connection Discovery from Event Images (pdf here)

Data was collected from pailixiang.com, a Chinese photo live platform. The event organizer uploads event images to the website during the event, and they are shared publicly online. Images do not come with information other than the upload time and the number of views. As there is no identity information available, faces are labeled with the identity manually using a custom-developed software. After manual labeling, there are over 3,000 participants labeled from over 40,000 faces and 8,837 images in the data set.

In the dataset:

1. ground.npy: 2 columns np array.
   1. 1st column: face id, formed by the image name+face count (e.g., GE3A1048_0, GE3A1048 is the image name in the dataset, and 0 indicate this is the first image detected)
   2. 2nd column: the person id of that face (e.g., 54781277. ‘-1’ indicates that the face cannot be identified, or it is not a face)

Note that the faces are detected using mtcnn

1. output.npz: the processed information of images and faces
   1. data_face_img: (160, 160, 3) of the original images
   2. data_e: embedding by facenet 128
   3. data_face_e_2: embedding by dlib
   4. data_e_vgg: embedding by vgg face
   5. data_ori_imgid: the source image id.
   6. timeTaken: the time that the image is taken
   7. data_face_score: omitted
   8. data_faceid: the face id in ground.npy
   9. face_score_2: face rating. 0 indicates that the face is in a bad condition that it is hardly identifiable (e.g., blur)

Instagram Faces Image dataset with diverse single-face images for facial recognition, anti-spoofing, and computer vision

This data is used in the second experimental evaluation of face smile detection in the paper titled "Smile detection using Hybrid Face Representaion" - O.A.Arigbabu et al. 2015.

Download the main images from LFWcrop website: http://conradsanderson.id.au/lfwcrop/ to select the samples we used for smile and non-smile, as in the list.

Kindly cite:

Arigbabu, Olasimbo Ayodeji, et al. "Smile detection using hybrid face representation." Journal of Ambient Intelligence and Humanized Computing (2016): 1-12.

C. Sanderson, B.C. Lovell. Multi-Region Probabilistic Histograms for Robust and Scalable Identity Inference. ICB 2009, LNCS 5558, pp. 199-208, 2009

Huang GB, Mattar M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: a database for studying face recognition in unconstrained environments. University of Massachusetts, Amherst, Technical Report

Introduction

Welcome to the Hispanic Human Face with Occlusion Dataset, meticulously curated to enhance face recognition models and support the development of advanced occlusion detection systems, biometric identification systems, KYC models, and other facial recognition technologies.

Facial Image Data

This dataset comprises over 3,000 human facial images, divided into participant-wise sets with each set including:

Diversity and Representation

The dataset includes contributions from a diverse network of individuals across Hispanic countries:

Quality and Conditions

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

Metadata

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

This metadata is essential for training models that can accurately recognize and identify human faces with occlusions across different demographics and conditions.

Usage and Applications

This facial image 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

Face Recognition, Face Detection, Male Photo Dataset 👨

  If you are interested in biometric data - visit our website to learn more and buy the dataset :)

110,000+ photos of 74,000+ men from 141 countries. The dataset includes photos of people's faces. All people presented in the dataset are men. The dataset contains a variety of images capturing individuals from diverse backgrounds and age groups. Our dataset will diversify your data by adding more photos of men of… See the full description on the dataset page: https://huggingface.co/datasets/TrainingDataPro/male-selfie-image-dataset.

Introduction

Welcome to the East Asian Child Faces Dataset, meticulously curated to enhance face recognition models and support the development of advanced biometric identification systems, child identification models, and other facial recognition technologies.

Facial Image Data

This dataset comprises over 5,000 child image sets, divided into participant-wise sets with each set including:

Diversity and Representation

The dataset includes contributions from a diverse network of children across East Asian countries:

Quality and Conditions

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

Metadata

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

This metadata is essential for training models that can accurately recognize and identify children's faces across different demographics and conditions.

Usage and Applications

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

Secure and Ethical Collection

Introduction

Welcome to the Middle Eastern Human Face with Occlusion Dataset, meticulously curated to enhance face recognition models and support the development of advanced occlusion detection systems, biometric identification systems, KYC models, and other facial recognition technologies.

Facial Image Data

This dataset comprises over 3,000 human facial images, divided into participant-wise sets with each set including:

Diversity and Representation

The dataset includes contributions from a diverse network of individuals across Middle Eastern countries:

Quality and Conditions

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

Metadata

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

This metadata is essential for training models that can accurately recognize and identify human faces with occlusions across different demographics and conditions.

Usage and Applications

This facial image 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

Here are a few use cases for this project:

1. Face recognition systems: Implement the "Faces" model to identify and recognize individuals in security systems, smartphone unlocking features, or attendance management systems.

2. Emotion analysis and sentiment detection: Use the "Faces" model to detect faces in images or videos, and then apply additional emotion recognition algorithms to determine the sentiment or emotional state of the subjects, aiding in fields like customer feedback analysis or behavioral research.

3. Smart photo organization: Utilize the "Faces" model to find and classify images in a photo library based on the presence of faces, allowing users to easily sort and organize their photos by individuals, events, or face-related criteria.

4. Social media content filtering and moderation: Implement the "Faces" model to automatically identify images and videos containing faces on social media platforms, enabling content moderation teams to focus on prioritizing privacy concerns, user consent, or violations of platform policies.

5. Non-verbal communication analysis: Use the "Faces" model to identify faces in video conferencing, interviews, or recorded events, enabling deeper analysis of non-verbal communication patterns, such as eye contact or micro-expressions, in order to provide insights into communicative effectiveness or cultural differences.

Description： 5,011 Images – Human Frontal face Data (Male). The data diversity includes multiple scenes, multiple ages and multiple races. This dataset includes 2,004 Caucasians , 3,007 Asians. This dataset can be used for tasks such as face detection, race detection, age detection, beard category classification.

Data size： 5,011 people, one image per person

Race distribution： 2,004 Caucasians , 3,007 Asians

Introduction

Welcome to the African Human Facial Images Dataset, meticulously curated to enhance face recognition models and support the development of advanced biometric identification systems, KYC models, and other facial recognition technologies.

Facial Image Data

This dataset comprises over 2,000 African individual facial image sets, with each set including:

Diversity and Representation

The dataset includes contributions from a diverse network of individuals across African countries.

Quality and Conditions

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

Metadata

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

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

Usage and Applications

This facial image 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 image dataset.

Welcome to Labeled Faces in the Wild, a database of face photographs designed for studying the problem of unconstrained face recognition. The data set contains more than 13,000 images of faces collected from the web. Each face has been labeled with the name of the person pictured. 1680 of the people pictured have two or more distinct photos in the data set. The only constraint on these faces is that they were detected by the Viola-Jones face detector.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1746215%2F92752ca2b0bbecdd3fd154b88495558d%2F1_RaupR7k7NrrTJZvop7sH-A.png?generation=1573849119616339&alt=media" alt="LFW-PEOPLE">

This dataset is a collection of JPEG pictures of famous people collected on the internet. All details are available on the official website: http://vis-www.cs.umass.edu/lfw/

Each picture is centered on a single face. Each pixel of each channel (color in RGB) is encoded by a float in range 0.0 - 1.0.

The task is called Face Recognition (or Identification): given the picture of a face, find the name of the person given a training set (gallery).

The original images are 250 x 250 pixels, but the default slice and resize arguments reduce them to 62 x 47 pixels.

Acknowledgements

We wouldn't be here without the help of others. I would like to thank Computer Vision Laboratory, university of Massachusetts for providing us with such an excellent database.

Inspiration

I had an activity in my college for facial recognition. I came up with this as the best kind of dataset for my task. I am posting it here on Kaggle to make it available for other data scientists conveniently and see what magic they can perform with this amazing dataset.

Face recognition is a biometric technology that identifies or verifies a person's identity by analyzing and comparing facial features from an image or video.This technology offers benefits such as enhanced security in access control, faster and more accurate identity verification, and improved convenience in applications like unlocking devices or streamlining airport check-ins. Additionally, it aids in law enforcement and surveillance, providing tools for crime prevention and public safety.

There are 3 images(fans1 , fans2 , image1) and a video(fansvideo) from football fans which can be used to evaluating face detection models.In addition , there is a Friends Actors images folder which contains All images and Actors folders which in the first one , there are 60 (ten images for each)images of 6 famous actors of Friends serial(Monica - Rachel - Phoebe-Ross - Joey - Chandler) and in the second folder, the actors have split to specific folders with their images .You can also use a video from Friends Serial (namely Friend.mp4 )to check your Recognizor model.

In case you are using SFace Recognition and YUnet Face Detection models , there are 2 ONNX files which one of them is face_detection_yunet_2023mar and the other is face_recognizer_fast.onn that you can use respectively.

background.jpg is just an image for background which additional.