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.

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

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.

"The Chicago Face Database was developed at the University of Chicago by Debbie S. Ma, Joshua Correll, and Bernd Wittenbrink. The CFD is intended for use in scientific research. It provides high-resolution, standardized photographs of male and female faces of varying ethnicity between the ages of 17-65. Extensive norming data are available for each individual model. These data include both physical attributes (e.g., face size) as well as subjective ratings by independent judges (e.g., attractiveness).

Detailed information about the construction of the database and the available norming data can be found in Ma, Correll, & Wittenbrink (2015)."

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.

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.

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.

Details about IMFDB: Indian Movie Face database (IMFDB) is a large unconstrained face database consisting of 34512 images of 100 Indian actors collected from more than 100 videos. All the images are manually selected and cropped from the video frames resulting in a high degree of variability interms of scale, pose, expression, illumination, age, resolution, occlusion, and makeup. IMFDB is the first face database that provides a detailed annotation of every image in terms of age, pose, gender, expression and type of occlusion that may help other face related applications.

This dataset is modified in such a way that it is ready for training a Face Recognition model. For dataset with annotations as mentioned above, you can download from here(official): https://cvit.iiit.ac.in/projects/IMFDB/

Acknowledgements: https://cvit.iiit.ac.in/projects/IMFDB/ Shankar Setty, Moula Husain, Parisa Beham, Jyothi Gudavalli, Menaka Kandasamy, Radhesyam Vaddi, Vidyagouri Hemadri, J C Karure, Raja Raju, Rajan, Vijay Kumar and C V Jawahar. "Indian Movie Face Database: A Benchmark for Face Recognition Under Wide Variations" National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 2013.

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

Dataset

This dataset was created by GIACOMO CAPITANI

Contents

Eye Position File Format - The eye position files are text files containing a single comment line followed by the x and the y coordinate of the left eye and the x and the y coordinate of the right eye separated by spaces. Note that we refer to the left eye as the person's left eye. Therefore, when captured by a camera, the position of the left eye is on the image's right and vice versa.

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.

Iranian Emotional Face Database is a set of images containing 248 face images displaying seven different facial emotional expressions including neutral, happy, sad, angry, disgusted, fearful and surprised. All images have been taken under the constant situation of lighting and camera set up from 40 individuals (25 male and 15 female in the age between 18-35).

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 is a database of faces and non-faces, that has been used extensively at the Center for Biological and Computational Learning at MIT. It is freely available for research use. CBCL FACE DATABASE #1: :- Includes complete Readme File :- 19 x 19 Grayscale PGM format images :- Training set: 2,429 faces, 4,548 non-faces :- Test set: 472 faces, 23,573 non-faces :- 27 Megabytes compressed : 110 Megabytes uncompressed :- tar / gz format compression Sponsors: This database is supported by the MIT Center for Biological and Compulational Learning. Keywords: Download, Database, Face, Non-face, Biological, Comuptaitonal, Research,

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.

The extended Yale Face Database B contains 16128 images of 28 human subjects under 9 poses and 64 illumination conditions. The data format of this database is the same as the Yale Face Database B. Please refer to the homepage of the Yale Face Database B for more detailed information of the data format. You are free to use the extended Yale Face Database B for research purposes. All publications which use this database should acknowledge the use of "the Exteded Yale Face Database B" and reference Athinodoros Georghiades, Peter Belhumeur, and David Kriegman s paper, "From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose", PAMI, 2001. The extended database as opposed to the original Yale Face Database B with 10 subjects was first reported by Kuang-Chih Lee, Jeffrey Ho, and David Kriegman in "Acquiring Linear Subspaces for Face Recognition under Variable Lighting, PAM

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.

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

The DOD Counterdrug Technology Program sponsored the Facial Recognition Technology (FERET) program and development of the FERET database. The National Institute of Standards and Technology (NIST) is serving as Technical Agent for distribution of the FERET database. The goal of the FERET program is to develop new techniques, technology, and algorithms for the automatic recognition of human faces. As part of the FERET program, a database of facial imagery was collected between December 1993 and August 1996. The database is used to develop, test, and evaluate face recognition algorithms.