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.

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.

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 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.

Infrared Face Detection Dataset

Dataset contains 125,500+ images, including infrared images, from 4,484 individuals with or without a mask of various races, genders, and ages. It is specifically designed for research in face recognition and facial recognition technology, focusing on the unique challenges posed by thermal infrared imaging. By utilizing this dataset, researchers and developers can enhance their understanding of recognition systems and improve the recognition accuracy… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/infrared-face-recognition-dataset.

Face Recognition 2.1

## Overview

Face Recognition 2.1 is a dataset for classification tasks - it contains Los5 annotations for 1,709 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 [MIT license](https://creativecommons.org/licenses/MIT).

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.

Face Recognition 1.0

## Overview

Face Recognition 1.0 is a dataset for classification tasks - it contains Face Clasificator annotations for 1,709 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 [MIT license](https://creativecommons.org/licenses/MIT).

Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('lfw', 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/lfw-0.1.1.png" alt="Visualization" width="500px">

Dataset of face images with different angles and head positions

Dataset contains 23,110 individuals, each contributing 28 images featuring various angles and head positions, diverse backgrounds, and attributes, along with 1 ID photo. In total, the dataset comprises over 670,000 images in formats such as JPG and PNG. It is designed to advance face recognition and facial recognition research, focusing on person re-identification and recognition systems. By utilizing this dataset… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/face-re-identification-image-dataset.

About this dataset

The Face Recognition Dataset is a collection of 2482 annotated images of human faces collected and labeled by Noor F. Abdul Hassan, Basrah University. This Dataset was created for the purpose of training on YOLO Models.

A dataset of 118 individuals with a variety of facial expressions and corresponding depth profiles.

While face recognition techniques have achieved remarkable performance in real- world applications, important issues still need to be addressed. Gender and race bias, as well as identity privacy problems, are among the top concerns due to their significant societal impact. Gender and race bias result in unequal accuracy between genders and across races. The identity privacy problem is related to the collection of training sets, as these sets are typically gathered without obtaining permission from the individuals represented in the dataset.

Our previous work has shown that facial attributes, such as facial hair, hairstyle, and face exposure, can significantly affect face recognition performance. We demon- strate that bias can be largely mitigated by balancing the distribution of these at- tributes in both the training set and the test set. The privacy problem has been exacerbated by government regulations (e.g., the General Data Privacy Regulation, or GDPR), which protect identity privacy but also hinder the development of more powerful face recognition techniques.

To address these problems, this proposed research aims to design a controlled face image generation model that can create images of non-existent identities to form a synthetic training set while controlling attribute distributions. After this, we notice that only pose and age variations are included in the test sets, which is insufficient to measure the intra-class variation of the generated training sets. To this end, we propose three test sets that focus on additional two attribute variations and identical twins. Lastly, we unlock the attribute control of the proposed model and conduct a comprehensive analysis to reveal the weaknesses of the existing synthetic face recognition datasets and provide insights for future work in this area.

Unidata’s Infrared Face Recognition dataset for improving security systems and enhancing AI performance in low-light condition

Discover the Event Face Recognition dataset, sourced from pailixiang.com, featuring 8,837 images and over 40,000 faces with 3,000+ labeled participants.

wuji3/face-recognition dataset hosted on Hugging Face and contributed by the HF Datasets community

face recogniton

The unavailability of a unified standard dataset for face mask detection and masked facial recognition motivated us to develop an in-house MDMFR dataset (MDMFR, 2022) to measure the performance of face mask detection and masked facial recognition methods. Both of these tasks have different dataset requirements. Face mask detection requires the images of multiple persons with and without mask. Whereas, masked face recognition requires multiple masked face images of the same person. Our MDMFR dataset consists of two main collections, 1) face mask detection, and 2) masked facial recognition. There are 6006 images in our MDMFR dataset. The face mask detection collection contains two categories of face images i.e., mask and unmask. Our detection database consists of 3174 with mask and 2832 without mask (unmasked) images. To construct the dataset, we captured multiple images of the same person in two configurations (mask and without mask). The masked facial recognition collection contains a total of 2896 masked images of 226 persons. More specifically, our dataset includes the images of both male and female persons of all ages including the children. The images of our dataset are diverse in terms of gender, race, and age of users, types of masks, illumination conditions, face angles, occlusions, environment, format, dimensions, and size, etc. Before being fed to our DeepMaskNet model, all images are scaled to a width and height of 256 pixels. All images have a bit depth of 24. We prepared the images of our dataset for the proposed DeepMaskNet model during preprocessing where images are cropped in Adobe-Photoshop to exclude the extra information like neck and shoulder. As the input size of our Deepmasknet model was 256-by-256, so images were resized to 256-by-256 in publicly available Plastiliq Image Resizer software (Plastiliq, 2022).

Black people Face Detection Dataset: 3M+ Identities

Large human faces dataset for face recognition models (10M+ images)

  Share with us your feedback and recieve additional samples for free!😊





  Full version of dataset is availible for commercial usage - leave a request on our website Axon Labs to purchase the dataset 💰

Dataset targeting 1:N and 1:1 NIST face recognition tests. Dataset contains 3M individuals, each with 3-5 images containing their faces The… See the full description on the dataset page: https://huggingface.co/datasets/AxonData/Black_People_Face_Recognition.

Dataset

This dataset was created by Yohanes07

Released under Other (specified in description)

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