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.

The PASCAL FACE dataset is a dataset for face detection and face recognition. It has a total of 851 images which are a subset of the PASCAL VOC and has a total of 1,341 annotations. These datasets contain only a few hundreds of images and have limited variations in face appearance.

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.

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.

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

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)

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 Face Detection Dataset and Benchmark (FDDB) dataset is a collection of labeled faces from Faces in the Wild dataset. It contains a total of 5171 face annotations, where images are also of various resolution, e.g. 363x450 and 229x410. The dataset incorporates a range of challenges, including difficult pose angles, out-of-focus faces and low resolution. Both greyscale and color images are included.

Dataset Card for Dataset Name

This dataset card aims to be a base template for new datasets. It has been generated using this raw template.

  Dataset Details





  Dataset Description

Curated by: [More Information Needed] Funded by [optional]: [More Information Needed] Shared by [optional]: [More Information Needed] Language(s) (NLP): [More Information Needed] License: [More Information Needed]

  Dataset Sources [optional]

Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/silk-road/IMDB-Face-Recognition.

Hey Guys.

Here I collect more than 2000 portrait faces of humans which are downloaded from the Google search engine and Pinterest and so on.

here you are able to upload your face and check it by deep learning model which is can detect whether your face is happy or sad.

file formats are : jpg - jpeg - png - svg

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.

Face

## Overview

Face is a dataset for object detection tasks - it contains Face annotations for 5,150 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).

genders

In this paper, we introduce a new large-scale face dataset named VGGFace2. The dataset contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession (e.g. actors, athletes, politicians). The dataset was collected with three goals in mind: (i) to have both a large number of identities and also a large number of images for each identity; (ii) to cover a large range of pose, age and ethnicity; and (iii) to minimise the label noise. We describe how the dataset was collected, in particular the automated and manual filtering stages to ensure a high accuracy for the images of each identity. To assess face recognition performance using the new dataset, we train ResNet-50 (with and without Squeeze-and-Excitation blocks) Convolutional Neural Networks on VGGFace2, on MS-Celeb-1M, and on their union, and show that training on VGGFace2 lead

SAFFIRE (South Asian Facial Features in Real Environments) is a high-quality annotated facial dataset designed for age, gender, occlusion, and pose estimation. It comprises diverse South Asian faces captured in real-world conditions, ensuring variability in lighting, background, and expressions. The dataset includes detailed annotations for facial attributes, making it valuable for robust AI model training. For further information and citation please refer to the paper: SAFFIRE: South Asian… See the full description on the dataset page: https://huggingface.co/datasets/Aakash941/SAFFIRE-Face-Dataset.

Proposes three types of masked face detection dataset; namely, the Correctly Masked Face Dataset (CMFD), the Incorrectly Masked Face Dataset (IMFD) and their combination for the global masked face detection (MaskedFace-Net).

The UTKFace dataset is a large-scale face dataset with long age span (range from 0 to 116 years old). The dataset consists of over 20,000 face images with annotations of age, gender, and ethnicity. The images cover large variation in pose, facial expression, illumination, occlusion, resolution, etc. This dataset could be used on a variety of tasks, e.g., face detection, age estimation, age progression/regression, landmark localization, etc.

UMDFaces is a face dataset divided into two parts:

Still Images - 367,888 face annotations for 8,277 subjects. Video Frames - Over 3.7 million annotated video frames from over 22,000 videos of 3100 subjects.

Part 1 - Still Images

The dataset contains 367,888 face annotations for 8,277 subjects divided into 3 batches. The annotations contain human curated bounding boxes for faces and estimated pose (yaw, pitch, and roll), locations of twenty-one keypoints, and gender information generated by a pre-trained neural network.

Part 2 - Video Frames

The second part contains 3,735,476 annotated video frames extracted from a total of 22,075 for 3,107 subjects. The annotations contain the estimated pose (yaw, pitch, and roll), locations of twenty-one keypoints, and gender information generated by a pre-trained neural network.

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.