Labeled Faces in the Wild, is 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. More details can be found in the technical report below.

The "Labeled Faces in the Wild-a" image collection is a database of labeled, face images intended for studying Face Recognition in unconstrained images. It contains the same images available in the original Labeled Faces in the Wild data set, however, here we provide them after alignment using a commercial face alignment software. Some of our results, published in [1,2,3], were produced using these images. We show this alignment to improve the performance of face recognition algorithms. More information on how these images were aligned may be found in the two papers. We have maintained the same directory structure as in the original LFW data set, and so these images can be used as direct substitutes for those in the original image set. Note, however, that the images available here are grayscale versions of the originals. Citation: If you find these images useful and use them in your work, please follow these guidlines: Comply with any instructions specified for the original L

The dataset comprises 16.7k images and 2 annotation files, each in a distinct format. The first file labeled Label contains annotations with the original scale.

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">

Face Label

## Overview

Face Label is a dataset for object detection tasks - it contains Faces annotations for 3,226 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).

Hello everyone , this is a dataset I am sharing , contains Happy and Non-Happy facial expressions to practice binary classification It contains labelled images of happy facial expression . I found this dataset while learning on coursera and I'd like to acknowledge them as the primary owner of the dataset

The LFW (Labeled Faces in the Wild) dataset is a popular benchmark dataset in the field of face recognition. It is used for evaluating and training face recognition algorithms and models.

The dataset contains 5,250 images with 11,931 annotated faces collected from the Internet. Each face contains the following annotations: square bounding box; pose deformation level of yaw, pitch and roll (small, medium, large); 'ignore' flag for faces which are smaller than 20x20 or extremely difficult to recognize (totally 838 faces, account for ~7%); other facial attributes: gender(female, male, unknown), isWearingGlasses, isOccluded and isExaggeratedExpression.

Our LSLF dataset consists of 1,195,976 labeled face images for 11,459 individuals. These images are stored in JPEG format with a total size of 5.36 GB. Individuals have a minimum of 1 face image and a maximum of 1,157 face images. The average number of face images per individual is 104. Each image is automatically named as (PersonName VideoNumber FrameNumber ImageNuumber) and stored in the related individual folder.

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

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.

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

WIDER FACE dataset is a face detection benchmark dataset, of which images are selected from the publicly available WIDER dataset. We choose 32,203 images and label 393,703 faces with a high degree of variability in scale, pose and occlusion as depicted in the sample images. WIDER FACE dataset is organized based on 61 event classes. For each event class, we randomly select 40%/10%/50% data as training, validation and testing sets. We adopt the same evaluation metric employed in the PASCAL VOC dataset. Similar to MALF and Caltech datasets, we do not release bounding box ground truth for the test images. Users are required to submit final prediction files, which we shall proceed to evaluate.

Context

We need a dataset with cropped faces to evaluate face verification algorithms. Because of that, I developed a python script to detect and crop all images from LFW ( http://vis-www.cs.umass.edu/lfw/index.html )

ONLY FOR DEVELOPMENT PURPOSE

Content

There are folders by people, some contains many faces, but others only contains one. Exists 13137 in the dataset

Inspiration

It's easy to get this faces, but could be (to me) a manner to save them and use it many times for different algorithms.

Data from the paper: "Unsupervised Joint Alignment of Complex Images" Gary B. Huang and Vidit Jain and Erik Learned-Miller ICCV 2007 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. More details can be found in the technical report below. Information: 13233 images 5749 people 1680 people with two or more images

Data from the paper: Learning to Align from Scratch Gary B. Huang and Marwan Mattar and Honglak Lee and Erik Learned-Miller NIPS 2012 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. More details can be found in the technical report below. Information: 13233 images 5749 people 1680 people with two or more images

The data presented here consists of three parts: Dataset 1: In this set, we extract 327,322 faces from our entire collection of 3389 issues, and automatically classified each face as male or female. We present this data as a single table with columns identifying the date, issue, page number, the coordinates identifying the position of the face on the page, and classification (male or female). The coordinates identifying the position of the face on the page are based on the size and resolution of the pages found in the “Time Vault”. Dataset 2: Dataset 2 consists of 8,789 classified faces from 100 selected issues. Human labor was used to identify and extract 3,299 face images from 39 issues, which were later classified by another set of workers. This selection of 39 issues contains one issue per decade spanned by the archive plus one issue per year between 1961 and 1991, and the extracted face images were used to train the face extraction algorithm. The remaining 5,490 faces from 61 issues were extracted via machine learning before being classified by human coders. These 61 issues were chosen to complement the first selection of 39 issues: one issue per year for all years in the archive excluding those between 1961 and 1991. Thus, Dataset 2 contains fully-labelled faces from at least one issue per year. Dataset 3: In the interest of transparency, Dataset 3 consists of the raw data collected to create Dataset 2, and consists of 2 tables. Before explaining these tables we first briefly describe our data collection and verification procedures, which have been fully described elsewhere. A custom AMT interface was used to enable human labors to classify faces according the categories in Table 4. Each worker was given a randomly-selected batch of 25 pages, each with a clearly highlighted face to be categorized, of which three pages were verification pages with known features, which were used for quality control. Each face was labeled by two distinct human coders, determined at random so that the paring of coders varied with the image. A proficiency rating was calculated for each coder by considering all images they annotated and computing the average number of labels that matched those identified by the image’s other coder. The tables in Dataset 2 were created by resolving inconsistencies between the two image coders by selecting the labels from the coder with the highest proficiency rating. Prior to calculating the proficiency score, all faces that were tagged as having ‘Poor’ or ‘Error’ image quality by either of the two coders were eliminated. Due to technical bugs when the AMT interface was first implemented, a small number of images were only labeled once; these were also eliminated from Datasets 2 and 3. In Dataset 3, we present the raw annotations for each coder that tagged each face, along with demographic data for each coder. Dataset 3 consists of two tables: the raw data from each of the two sets of coders, and the demographic information for each of the coders.

Face Detection With Yolov8

## Overview

Face Detection With Yolov8 is a dataset for object detection tasks - it contains Face annotations for 3,479 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).

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.

The FaciaVox dataset is an extensive multimodal biometric resource designed to enable in-depth exploration of face-image and voice recording research areas in both masked and unmasked scenarios.

Features of the Dataset:

1. Multimodal Data: A total of 1,800 face images (JPG) and 6,000 audio recordings (WAV) were collected, enabling cross-domain analysis of visual and auditory biometrics.

2. Participants were categorized into four age groups for structured labeling:
Label 1: Under 16 years
Label 2: 16 to less than 31 years
Label 3: 31 to less than 46 years
Label 4: 46 years and above

3. Sibling Data: Some participants are siblings, adding a challenging layer for speaker identification and facial recognition tasks due to genetic similarities in vocal and facial features. Sibling relationships are documented in the accompanying "FaciaVox List" data file.

4. Standardized Filenames: The dataset uses a consistent, intuitive naming convention for both facial images and voice recordings. Each filename includes:
Type (F: Face Image, V: Voice Recording)
Participant ID (e.g., sub001)
Mask Type (e.g., a: unmasked, b: disposable mask, etc.)
Zoom Level or Sentence ID (e.g., 1x, 3x, 5x for images or specific sentence identifier {01, 02, 03, ..., 10} for recordings)

5. Diverse Demographics: 19 different countries.

6. A challenging face recognition problem involving reflective mask shields and severe lighting conditions.

7. Each participant uttered 7 English statements and 3 Arabic statements, regardless of their native language. This adds a challenge for speaker identification.

Research Applications

FaciaVox is a versatile dataset supporting a wide range of research domains, including but not limited to:
• Speaker Identification (SI) and Face Recognition (FR): Evaluating biometric systems under varying conditions.
• Impact of Masks on Biometrics: Investigating how different facial coverings affect recognition performance.
• Language Impact on SI: Exploring the effects of native and non-native speech on speaker identification.
• Age and Gender Estimation: Inferring demographic information from voice and facial features.
• Race and Ethnicity Matching: Studying biometrics across diverse populations.
• Synthetic Voice and Deepfake Detection: Detecting cloned or generated speech.
• Cross-Domain Biometric Fusion: Combining facial and vocal data for robust authentication.
• Speech Intelligibility: Assessing how masks influence speech clarity.
• Image Inpainting: Reconstructing occluded facial regions for improved recognition.

Researchers can use the facial images and voice recordings independently or in combination to explore multimodal biometric systems. The standardized filenames and accompanying metadata make it easy to align visual and auditory data for cross-domain analyses. Sibling relationships and demographic labels add depth for tasks such as familial voice recognition, demographic profiling, and model bias evaluation.