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.

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

High-resolution thermal infrared face database with extensive manual annotations, introduced by Kopaczka et al, 2018. Useful for training algoeithms for image processing tasks as well as facial expression recognition. The full database itself, all annotations and the complete source code are freely available from the authors for research purposes at https://github.com/marcinkopaczka/thermalfaceproject.

Please cite following papers for the dataset: [1] M. Kopaczka, R. Kolk and D. Merhof, "A fully annotated thermal face database and its application for thermal facial expression recognition," 2018 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2018, pp. 1-6, doi: 10.1109/I2MTC.2018.8409768. [2] Kopaczka, M., Kolk, R., Schock, J., Burkhard, F., & Merhof, D. (2018). A thermal infrared face database with facial landmarks and emotion labels. IEEE Transactions on Instrumentation and Measurement, 68(5), 1389-1401.

FGnet Markup Scheme of the BioID Face Database - The BioID Face Database is being used within the FGnet project of the European Working Group on face and gesture recognition. David Cristinacce and Kola Babalola, PhD students from the department of Imaging Science and Biomedical Engineering at the University of Manchester marked up the images from the BioID Face Database. They selected several additional feature points, which are very useful for facial analysis and gesture recognition.

Expressions

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.

SCface is a database of static images of human faces. Images were taken in uncontrolled indoor environment using five video surveillance cameras of various qualities. Database contains 4160 static images (in visible and infrared spectrum) of 130 subjects. Images from different quality cameras mimic the real-world conditions and enable robust face recognition algorithms testing, emphasizing different law enforcement and surveillance use case scenarios.

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

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.

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.

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

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.

Georgia Tech face database (128MB) contains images of 50 people taken in two or three sessions between 06/01/99 and 11/15/99 at the Center for Signal and Image Processing at Georgia Institute of Technology. All people in the database are represented by 15 color JPEG images with cluttered background taken at resolution 640x480 pixels. The average size of the faces in these images is 150x150 pixels. The pictures show frontal and/or tilted faces with different facial expressions, lighting conditions and scale. Each image is manually labeled to determine the position of the face in the image. The set of label files is available here. The Readme.txt file gives more details about the database.

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.

Description

The Dark Face dataset provides 6,000 real-world low light images captured during the nighttime, at teaching buildings, streets, bridges, overpasses, parks, etc., all labeled with bounding boxes for of human face, as the main training and/or validation sets. We also provide 9,000 unlabeled low-light images collected from the same setting. Additionally, we provided a unique set of 789 paired low-light/normal-light images captured in controllable real lighting conditions (but unnecessarily containing faces), which can be used as parts of the training data at the participants' discretization. There will be a hold-out testing set of 4,000 low-light images, with human face bounding boxes annotated.

Credits: Spatial and Temporal Restoration, Understanding and Compression Team, Wangxuan institute of computer technology, Peking University.

Citation

@ARTICLE{poor_visibility_benchmark,
 author={Yang, Wenhan and Yuan, Ye and Ren, Wenqi and Liu, Jiaying and Scheirer, Walter J. and Wang, Zhangyang and Zhang, and et al.},
 journal={IEEE Transactions on Image Processing}, 
 title={Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study}, 
 year={2020},
 volume={29},
 number={},
 pages={5737-5752},
 doi={10.1109/TIP.2020.2981922}
}

@inproceedings{Chen2018Retinex,
    title={Deep Retinex Decomposition for Low-Light Enhancement},
    author={Chen Wei, Wenjing Wang, Wenhan Yang, Jiaying Liu},
    booktitle={British Machine Vision Conference},
    year={2018},
}

The Database of Faces

Our Database of Faces, (formerly 'The ORL Database of Faces'), contains a set of face images taken between April 1992 and April 1994 at the lab. The database was used in the context of a face recognition project carried out in collaboration with the Speech, Vision and Robotics Group of the Cambridge University Engineering Department.

There are ten different images of each of 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses). All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement). A preview image of the Database of Faces is available.

The files are in PGM format, and can conveniently be viewed on UNIX (TM) systems using the 'xv' program. The size of each image is 92x112 pixels, with 256 grey levels per pixel. The images are organised in 40 directories (one for each subject), which have names of the form sX, where X indicates the subject number (between 1 and 40). In each of these directories, there are ten different images of that subject, which have names of the form Y.pgm, where Y is the image number for that subject (between 1 and 10).

The database can be retrieved from http://www.cl.cam.ac.uk/Research/DTG/attarchive:pub/data/att_faces.tar.Z as a 4.5Mbyte compressed tar file or from http://www.cl.cam.ac.uk/Research/DTG/attarchive:pub/data/att_faces.zip as a ZIP file of similar size.

A convenient reference to the work using the database is the paper Parameterisation of a stochastic model for human face identification. Researchers in this field may also be interested in the author's PhD thesis, Face Recognition Using Hidden Markov Models, available from http://www.cl.cam.ac.uk/Research/DTG/attarchive/pub/data/fsamaria_thesis.ps.Z (~1.7 MB).

When using these images, please give credit to AT&T Laboratories Cambridge.

UNIX is a trademark of UNIX System Laboratories, Inc.

Contact information Copyright © 2002 AT&T Laboratories Cambridge

Credit: https://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

CASIA-Face-Africa is a face image database which contains 38,546 images of 1,183 African subjects. Multi-spectral cameras are utilized to capture the face images under various illumination settings. Demographic attributes and facial expressions of the subjects are also carefully recorded. For landmark detection, each face image in the database is manually labeled with 68 facial keypoints. A group of evaluation protocols are constructed according to different applications, tasks, partitions and scenarios. The proposed database along with its face landmark annotations, evaluation protocols and preliminary results form a good benchmark to study the essential aspects of face biometrics for African subjects, especially face image preprocessing, face feature analysis and matching, facial expression recognition, sex/age estimation, ethnic classification, face image generation, etc.

This is the largest database of hyperspectral face images containing hyperspectral image cubes of 78 subjects imaged in multiple sessions. The data was captured with the CRI's VariSpec LCTF (Liquid Crystal Tunable Filter) integrated with a Photon Focus machine vision camera. There are 33 spectral bands comering the 400 - 720nm range with a 10nm step. The noise level in the dataset is relatively lower because we adapted the camera exposure time to the transmittance of the filter illumination intensity as well as CCD sensitivity in each band.

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

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