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.

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.

Images are of 102 adult faces 1350x1350 pixels in full colour. Template files mark out 189 coordinates delineating face shape, for use with Psychomorph or WebMorph.org.Self-reported age, gender and ethnicity are included in the file london_faces_info.csv. Attractiveness ratings (on a 1-7 scale from "much less attractiveness than average" to "much more attractive than average") for the neutral front faces from 2513 people (ages 17-90) are included in the file london_faces_ratings.csv.All individuals gave signed consent for their images to be "used in lab-based and web-based studies in their original or altered forms and to illustrate research (e.g., in scientific journals, news media or presentations)." Images were taken in London, UK, in April 2012.

The Extended Yale B database contains 2414 frontal-face images with size 192×168 over 38 subjects and about 64 images per subject. The images were captured under different lighting conditions and various facial expressions.

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.

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

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

The Sheffield (previously UMIST) Face Database consists of 564 images of 20 individuals (mixed race/gender/appearance). Each individual is shown in a range of poses from profile to frontal views – each in a separate directory labelled 1a, 1b, … 1t and images are numbered consecutively as they were taken. The files are all in PGM format, approximately 220 x 220 pixels with 256-bit grey-scale.

The average recognition rates (%) and the corresponding standard deviations (%) of different algorithms on the test set of the AR face database with sunglasses and scarf occlusions (sub-image size 32×32).

Comparisons of CPU time on AR face database testing with scarves.

The MAPIR Faces Dataset (publication still pending) is a collection of face images from 16 different individuals. Each individual has approximately 49 images uniformly distributed in a 7x7 grid over the head pose space defined by:

• Pitch \(\in (-65.0,~65.0)\)
• Yaw \(\in (-35.0,~35.0)\)

This dataset is designed as a benchmark to analyze the effect of detrimental factors due to pose variance in face recognition algorithms.

Same as cropped images here, just converted to PNG instead http://vision.ucsd.edu/~leekc/ExtYaleDatabase/ExtYaleB.html

I do not own this data. All credits go to:

"From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose", PAMI, 2001, "Acquiring Linear Subspaces for Face Recognition under Variable Lighting", PAMI, May, 2005 "the Extended Yale Face Database B"

The cropped dataset only contains the single P00 pose.

Data format is like yaleBxx_P00A(+/-)aaaE(+/-)ee

• xx = Subject ID
• (+/-)aaa = Azimuth angle
• (+/-)ee = Elevation angle

For example the file yaleB38_P00A+035E+65.png is of subject 38, in pose 00, with light source at (+035, +65) degrees (azimuth, elevation) w.r.t the camera.

The overall aim of this project is to enable the next generation of research into face perception and recognition by making available a rich database of face images, including many 3 dimensional scans, and the tools to manipulate and present them.There are many existing collections of face images, usually collected for a specific project and often either very consistent or very varied, limiting their general utility. The aim here is to collect both highly consistent and highly varied images of the same people, including 3D scans, both standard and stereo video sequences, and a variety of photographs. 3D imagery is becoming more common but the images remain difficult to use. The second part of this project is therefore to develop tools for handling 3d images, for example morphing between two faces to create an average, or making a given face look older, or more trustworthy. Finally, it will make available two different software packages for presenting 3D faces in experiments. It is hoped that the database will gradually grow in value as researchers contribute their data, for example ratings of faces.

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.

Ahmad Ilham Simple Face Database

Description

The ✨🌸 Anime Face Dataset 🌸✨ is a comprehensive collection of high-quality images of popular anime characters. This dataset is ideal for training machine learning models for facial recognition, emotion detection, and other applications in the field of anime character analysis. Each folder within the dataset contains images of a specific character, ensuring that all images are categorized and easy to access.

Real-World Masked Face Dataset (RMFD) is a large dataset for masked face detection.

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.

This dataset of friends series character sets for face detection and recognition consists of two parts: training and testing. The training section has fifty photos of each of the six characters, and the test section has fifty photos featuring two or more of the characters in each photo from the Friends series for facial detection and recognition.