Fake Face Vs Real Face

## Overview

Fake Face Vs Real Face is a dataset for object detection tasks - it contains Fake Face annotations for 494 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).

AI-generated, high-quality face datasets. Based on model-released photos. Diverse expressions, ethnicities, and age groups. Excellent for face recognition and analysis projects.

WIDER FACE dataset is a face detection benchmark dataset, of which images are selected from the publicly available WIDER dataset. We choose 32,203 images and label 393,703 faces with a high degree of variability in scale, pose and occlusion as depicted in the sample images. WIDER FACE dataset is organized based on 61 event classes. For each event class, we randomly select 40%/10%/50% data as training, validation and testing sets. We adopt the same evaluation metric employed in the PASCAL VOC dataset. Similar to MALF and Caltech datasets, we do not release bounding box ground truth for the test images. Users are required to submit final prediction files, which we shall proceed to evaluate.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('wider_face', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/wider_face-0.1.0.png" alt="Visualization" width="500px">

Introduction

Welcome to the Native American Facial Images from Past 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 5,000+ images, divided into participant-wise sets with each set including:

Diversity and Representation

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

Quality and Conditions

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

Metadata

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

This metadata is essential for training models that can accurately recognize and identify Native American 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

Description

K-pop Idol Dataset - Female (KID-F) is the first dataset of K-pop idol high quality face images. It consists of about 6,000 high quality face images at 512x512 resolution and identity labels for each image.

We collected about 90,000 K-pop female idol images and crop the face from each image. And we classified high quality face images. As a result, there are about 6,000 high quality face images in this dataset.

There are 300 test datasets for a benchmark. There are no duplicate images between test and train images. Some identities in test images are not duplicated with train images. (It means some test images is new identity to the trained model) Each test images have its degraded pair. You can use these degraded test images for testing face super resolution performance.

We also provide identity labels for each image.

You can use this dataset for training face super resolution models.

Agreement

• The use of this software is RESTRICTED to non-commercial research and educational purposes.
• All images of the KID-F dataset are obtained from the internet which are not property of EDA(PCEO-AI-CLUB). EDA 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 KID-F dataset. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset.
• EDA reserves the right to terminate your access to the CelebA dataset at any time.

The BioID Face Database has been recorded and is published to give all researchers working in the area of face detection the possibility to compare the quality of their face detection algorithms with others. During the recording special emphasis has been laid on real world conditions. Therefore the testset features a large variety of illumination, background and face size. The dataset consists of 1521 gray level images with a resolution of 384x286 pixel. Each one shows the frontal view of a face of one out of 23 different test persons. For comparison reasons the set also contains manually set eye postions. The images are labeled BioID_xxxx.pgm where the characters xxxx are replaced by the index of the current image (with leading zeros). Similar to this, the files BioID_xxxx.eye contain the eye positions for the corresponding images.

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. More details can be found in the technical report below. Information: 13233 images 5749 people 1680 people with two or more images Citation: Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments Gary B. Huang and Manu Ramesh and Tamara Berg and Erik Learned-Miller University of Massachusetts, Amherst - 2007

Example Faces

## Overview

Example Faces is a dataset for object detection tasks - it contains Face annotations for 888 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 LFW (Labeled Faces in the Wild) dataset is a popular benchmark dataset in the field of face recognition. It is used for evaluating and training face recognition algorithms and models.

Description Human Face Segmentation Data from 70,846 Images. Pure color backgrounds, interior and exterior scene types are all included in the data. Both males and females are included in the data. Asian, Black, and Caucasian races are represented in the race distribution. The age ranges from young children to elderly people. Simple and complex facial expressions can be found in the data (large-angle tilt of face, closing eye, glower, pucker, opening mouth, etc.). We used pixel-by-pixel segmentation annotations to annotate the human face, the five sense organs, the body, and appendages. The information can be applied to tasks like facial Recon Related Tasks For more details, please visit: https://www.nexdata.ai/datasets/computervision/945?source=Kaggle

Specifications Data size 70,846 images, there is only one face in an image Population distribution race distribution: 32,235 images of Asian, 29,501 images of Caucasian, 9,110 images of black race; gender distribution: 34,044 male images and 36,802 female images; age distribution: baby, teenager, young, midlife and senior Collection environment including pure color background, indoor scenes and outdoor scenes Data diversity multiple scenes, multiple ages, multiple races, complicated expressions (closing eye, glower, pucker, opening mouth, etc.), and multiple appendages Image Parameter Data format: the image data is in .jpg or .png format, the annotation file is in .json or .psd format; the human face resolution is not lower than 128*128, and pupillary distance is not less than 60 pixels Annotation content segmentation annotation of human face, the five sense organs, body and appendages Accuracy the mask edge location errors in x and y directions are less than 3 pixels, which is considered as a qualified annotation; the annotation part (id) is regarded as the unit, the accuracy rate of segmentation annotation shall be more than 97%

Get the Dataset This is just an example of the data. To access more sample data or request the price, contact us at info@nexdata.ai

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.

CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including - 10,177 number of identities, - 202,599 number of face images, and - 5 landmark locations, 40 binary attributes annotations per image.

The dataset can be employed as the training and test sets for the following computer vision tasks: face attribute recognition, face detection, and landmark (or facial part) localization.

Note: CelebA dataset may contain potential bias. The fairness indicators example goes into detail about several considerations to keep in mind while using the CelebA dataset.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('celeb_a', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/celeb_a-2.1.0.png" alt="Visualization" width="500px">

Includes videos of 11 subjects, each showing 18 different angles of their face for one second each. The process was repeated with 5 light settings (warm, cold, low, medium, and bright). Videos are recorded in 3840 pixels tall by 2160 pixels wide and are saved in .MP4 format.

Human Face

## Overview

Human Face is a dataset for object detection tasks - it contains Face annotations for 727 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 [Public Domain license](https://creativecommons.org/licenses/Public Domain).

Introduction

Welcome to the East Asian Facial Images from Past 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 10,000+ images, divided into participant-wise sets with each set including:

Diversity and Representation

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

Quality and Conditions

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

Metadata

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

This metadata is essential for training models that can accurately recognize and identify East Asian 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

TrueFace is a first dataset of social media processed real and synthetic faces, obtained by the successful StyleGAN generative models, and shared on Facebook, Twitter and Telegram.

Images have historically been a universal and cross-cultural communication medium, capable of reaching people of any social background, status or education. Unsurprisingly though, their social impact has often been exploited for malicious purposes, like spreading misinformation and manipulating public opinion. With today's technologies, the possibility to generate highly realistic fakes is within everyone's reach. A major threat derives in particular from the use of synthetically generated faces, which are able to deceive even the most experienced observer. To contrast this fake news phenomenon, researchers have employed artificial intelligence to detect synthetic images by analysing patterns and artifacts introduced by the generative models. However, most online images are subject to repeated sharing operations by social media platforms. Said platforms process uploaded images by applying operations (like compression) that progressively degrade those useful forensic traces, compromising the effectiveness of the developed detectors. To solve the synthetic-vs-real problem "in the wild", more realistic image databases, like TrueFace, are needed to train specialised detectors.

The ChokePoint dataset is designed for experiments in person identification/verification under real-world surveillance conditions using existing technologies. An array of three cameras was placed above several portals (natural choke points in terms of pedestrian traffic) to capture subjects walking through each portal in a natural way. While a person is walking through a portal, a sequence of face images (ie. a face set) can be captured. Faces in such sets will have variations in terms of illumination conditions, pose, sharpness, as well as misalignment due to automatic face localisation/detection. Due to the three camera configuration, one of the cameras is likely to capture a face set where a subset of the faces is near-frontal.

The dataset consists of 25 subjects (19 male and 6 female) in portal 1 and 29 subjects (23 male and 6 female) in portal 2. The recording of portal 1 and portal 2 are one month apart. The dataset has frame rate of 30 fps and the image resolution is 800X600 pixels. In total, the dataset consists of 48 video sequences and 64,204 face images. In all sequences, only one subject is presented in the image at a time. The first 100 frames of each sequence are for background modelling where no foreground objects were presented.

Each sequence was named according to the recording conditions (eg. P2E_S1_C3) where P, S, and C stand for portal, sequence and camera, respectively. E and L indicate subjects either entering or leaving the portal. The numbers indicate the respective portal, sequence and camera label. For example, P2L_S1_C3 indicates that the recording was done in Portal 2, with people leaving the portal, and captured by camera 3 in the first recorded sequence.

To pose a more challenging real-world surveillance problems, two seqeunces (P2E_S5 and P2L_S5) were recorded with crowded scenario. In additional to the aforementioned variations, the sequences were presented with continuous occlusion. This phenomenon presents challenges in identidy tracking and face verification.

This dataset can be applied, but not limited, to the following research areas:

person re-identification

image set matching

face quality measurement

face clustering

3D face reconstruction

pedestrian/face tracking

background estimation and subtraction

Please cite the following paper if you use the ChokePoint dataset in your work (papers, articles, reports, books, software, etc):

Y. Wong, S. Chen, S. Mau, C. Sanderson, B.C. Lovell Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition IEEE Biometrics Workshop, Computer Vision and Pattern Recognition (CVPR) Workshops, pages 81-88, 2011. http://doi.org/10.1109/CVPRW.2011.5981881

Cow Face

## Overview

Cow Face is a dataset for object detection tasks - it contains Cow Face annotations for 374 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).

Includes face images of 11 subjects with 3 sets of images: one of the subject with no occlusion, one of them wearing a hat, and one of them wearing glasses. Each set consists of 5 subject positions (subject's two profile positions, one central position, and two positions angled between the profile and central positions), with 7 lighting angles for each position (completing a 180 degree arc around the subject), and 5 light settings for each angle (warm, cold, low, medium, and bright). Images are 5184 pixels tall by 3456 pixels wide and are saved in .JPG format.