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.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('wider_face', 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/wider_face-0.1.0.png" alt="Visualization" width="500px">

Sample Wider Face

## Overview

Sample Wider Face is a dataset for object detection tasks - it contains Face annotations for 10,000 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).

WIDER FACE 3000

## Overview

WIDER FACE 3000 is a dataset for object detection tasks - it contains Face annotations for 3,000 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).

Dataset

This dataset was created by AkuMasihPemula

Released under MIT

Contents

The Wider Facial Landmarks in the Wild or WFLW database contains 10000 faces (7500 for training and 2500 for testing) with 98 annotated landmarks. This database also features rich attribute annotations in terms of occlusion, head pose, make-up, illumination, blur and expressions.

license: mit language: en tags:

computer-vision face-detection image-classification

  Cropped Faces from WIDER FACE Dataset





  Dataset Description

This repository provides two key datasets for face-related computer vision tasks, delivered as two separate .zip archives:

WIDER_val.zip: A compressed archive of the original validation set from the well-known WIDER FACE dataset. It contains 3,226 images with a wide variety of scales, poses, and occlusions.… See the full description on the dataset page: https://huggingface.co/datasets/amannagrawall002/croppedFaceDataset.

Dataset

This dataset was created by Duc Hoa

Contents

WIDER Valid

## Overview

WIDER Valid is a dataset for object detection tasks - it contains Face 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).

Face Recognition, Face Detection, Female Photo Dataset 👩

If you are interested in biometric data - visit our website to learn more and buy the dataset :)

90,000+ photos of 46,000+ women from 141 countries. The dataset includes photos of people's faces. All people presented in the dataset are women. The dataset contains a variety of images capturing individuals from diverse backgrounds and age groups.

Our dataset will diversify your data by adding more photos of women of different ages and ethnic groups, enhancing the quality of your model.

People in the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fd1b31bcda4a90b808473dbe5970bebfb%2FFrame%20108.png?generation=1714148221118707&alt=media" alt="">

The dataset can be utilized for a wide range of tasks, including face recognition, age estimation, image feature extraction, or any problem related to human image analysis.

💴 For Commercial Usage: Full version of the dataset includes 90,000+ photos of people, leave a request on TrainingData to buy the dataset

Metadata for the dataset:

• id - unique identifier of the media file
• photo - link to access the photo,
• age - age of the person
• gender - gender of the person
• country - country of the person
• ethnicity - ethnicity of the person
• photo_extension - photo extension,
• photo_resolution - photo resolution

Statistics for the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F2796accc4d7b47e8e1ac02701f4eac7b%2FFemale%20Images.png?generation=1714147921067232&alt=media" alt="">

💴 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 consists of: - files - includes 20 images corresponding to each person in the sample, - .csv file - contains information about the images and people in the dataset

File with the extension .csv

• id: id of the person,
• age - age of the person,
• country - country of the person,
• ethnicity - ethnicity of the person,
• photo_extension: extension of the photo,
• photo_resolution: photo_resolution of the photo

TrainingData provides high-quality data annotation tailored to your needs

keywords: biometric system, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, object detection dataset, deep learning datasets, computer vision datset, human images dataset, human faces dataset, machine learning, image-to-image, verification models, digital photo-identification, women images, females dataset, female selfie, female face recognition

YOLOv8 Detection Model

  Datasets





  Face

Anime Face CreateML xml2txt AN wider face

  Hand

AnHDet hand-detection-fuao9

  Person

coco2017 (only person) AniSeg skytnt/anime-segmentation

  deepfashion2

deepfashion2

id label

0 short_sleeved_shirt

1 long_sleeved_shirt

2 short_sleeved_outwear

3 long_sleeved_outwear

4 vest

5 sling

6 shorts

7 trousers

8 skirt

9 short_sleeved_dress

10 long_sleeved_dress

11… See the full description on the dataset page: https://huggingface.co/datasets/Blankse/SegsmakerAdetailer.

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.

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.

The global face recognition solution market is experiencing robust growth, driven by increasing adoption across various sectors. While precise figures for market size and CAGR are unavailable from the provided text, we can infer significant expansion based on prevalent industry trends. The market's expansion is fueled by several key factors. Firstly, the rising demand for enhanced security measures in various sectors, including law enforcement, border control, and access control systems, is a primary driver. Secondly, advancements in artificial intelligence (AI) and deep learning technologies are continuously improving the accuracy and efficiency of face recognition systems, leading to wider adoption. The integration of face recognition into mobile devices and cloud-based platforms further expands its reach and usability. Moreover, the increasing availability of high-quality cameras and the declining cost of computational power are making face recognition solutions more accessible and cost-effective. Despite the significant growth potential, the market faces certain restraints. Concerns regarding privacy and data security are paramount, as the improper use of facial recognition data can lead to ethical dilemmas and legal ramifications. Regulatory hurdles and data protection laws vary across regions, posing challenges for market expansion. Furthermore, the accuracy and reliability of face recognition systems can be impacted by factors such as lighting conditions, facial expressions, and image quality. Market segmentation reveals a diverse application landscape, spanning security & surveillance, identity verification, law enforcement, and various other domains. Similarly, the types of solutions vary, including software, hardware, and cloud-based offerings. Geographical analysis suggests a strong market presence across North America, Europe, and Asia-Pacific, with continued growth expected across emerging economies. The long-term outlook remains positive, with projected steady growth throughout the forecast period (2025-2033), driven by continuous technological advancements and increased adoption across numerous applications.

The global face recognition solution market is experiencing robust growth, driven by increasing adoption across diverse sectors. The market, estimated at $15 billion in 2025, is projected to exhibit a compound annual growth rate (CAGR) of 15% from 2025 to 2033, reaching an estimated $50 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising demand for enhanced security measures in various applications, including access control, law enforcement, and border security, is a major catalyst. Secondly, advancements in artificial intelligence (AI) and deep learning technologies are leading to improved accuracy and efficiency of face recognition systems. Thirdly, the decreasing cost of hardware and software components is making these solutions more accessible to a wider range of businesses and organizations. Furthermore, the increasing integration of face recognition technology into smartphones and other consumer devices is fueling market growth. The market is segmented by application (e.g., law enforcement, access control, time and attendance) and type (e.g., hardware, software, services). North America currently holds a significant market share, followed by Europe and Asia-Pacific, which are expected to witness substantial growth in the coming years. However, the market faces certain restraints. Privacy concerns surrounding the use of facial recognition technology remain a significant challenge. Data security breaches and potential misuse of the technology raise ethical and legal issues that require careful consideration. Regulatory hurdles and varying data protection laws across different regions also pose a significant challenge for market expansion. Despite these constraints, the long-term outlook for the face recognition solution market remains positive, driven by ongoing technological advancements, increasing demand for security solutions, and the growing adoption of AI-powered applications. The competitive landscape is dynamic, with several established players and emerging companies vying for market share through innovation and strategic partnerships.

The global market for MCU-based solutions for 3D face recognition is experiencing robust growth, projected to reach $1.312 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 6.0% from 2025 to 2033. This expansion is driven by several key factors. The increasing demand for secure authentication across various sectors, including consumer electronics (smartphones, laptops), automotive (access control, driver identification), BFSI (fraud prevention, enhanced security), and smart homes (access control systems), is a major catalyst. Furthermore, advancements in 3D sensing technology, leading to more accurate and reliable face recognition systems, are fueling market growth. The miniaturization of microcontrollers (MCUs) and the decreasing cost of components are also contributing to the wider adoption of these solutions. Competitive pressures among leading technology companies, such as Intel, Hikvision, and OMRON, are driving innovation and pushing down prices, making the technology accessible to a broader range of applications. However, certain restraints are present. Data privacy and security concerns remain significant hurdles, particularly concerning the ethical implications of widespread facial recognition deployment and potential misuse. The need for robust data protection measures and transparent data handling practices is crucial for sustained market growth. Moreover, the technical complexities associated with implementing accurate and reliable 3D face recognition systems, especially in diverse lighting conditions and with varying user characteristics, pose a challenge. Addressing these concerns through the development of more sophisticated algorithms and user-friendly interfaces is vital for wider market acceptance and continued expansion. Despite these challenges, the long-term outlook for the MCU-based 3D face recognition market remains positive, fueled by consistent technological advancements and the growing demand for secure authentication across multiple sectors.

Custom WiderFace Dataset

## Overview

Custom WiderFace Dataset is a dataset for object detection tasks - it contains Face annotations for 1,644 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).

Biometric Attack Dataset, Asian People

  The similar dataset that includes all ethnicities - Anti Spoofing Real Dataset

The dataset for face anti spoofing and face recognition includes images and videos of asian people. 30,600+ photos & video of 15,300 people from 32 countries. All people presented in the dataset are South Asian, East Asian or Middle Asian. The dataset helps in enchancing the performance of the model by providing wider range of data for a specific ethnic… See the full description on the dataset page: https://huggingface.co/datasets/TrainingDataPro/asian-people-liveness-detection-video-dataset.

The 300-W is a face dataset that consists of 300 Indoor and 300 Outdoor in-the-wild images. It covers a large variation of identity, expression, illumination conditions, pose, occlusion and face size. The images were downloaded from google.com by making queries such as “party”, “conference”, “protests”, “football” and “celebrities”. Compared to the rest of in-the-wild datasets, the 300-W database contains a larger percentage of partially-occluded images and covers more expressions than the common “neutral” or “smile”, such as “surprise” or “scream”. Images were annotated with the 68-point mark-up using a semi-automatic methodology. The images of the database were carefully selected so that they represent a characteristic sample of challenging but natural face instances under totally unconstrained conditions. Thus, methods that achieve accurate performance on the 300-W database can demonstrate the same accuracy in most realistic cases. Many images of the database contain more than one annotated faces (293 images with 1 face, 53 images with 2 faces and 53 images with [3, 7] faces). Consequently, the database consists of 600 annotated face instances, but 399 unique images. Finally, there is a large variety of face sizes. Specifically, 49.3% of the faces have size in the range [48.6k, 2.0M] and the overall mean size is 85k (about 292 × 292) pixels.

This Wider-Test-200 dataset is introduced in the following paper: "Towards Unsupervised Blind Face Restoration using Diffusion Prior"

Please visit our website and refer to our paper for more information on the dataset and our method: https://dt-bfr.github.io/

Forensic facial identification examiners are required to match the identity of faces in images that vary substantially, owing to changes in viewing conditions and in a person's appearance. These identifications affect the course and outcome of criminal investigations and convictions. Despite calls for research on sources of human error in forensic examination, existing scientific knowledge of face matching accuracy is based, almost exclusively, on people without formal training. Here, we administered three challenging face matching tests to a group of forensic examiners with many years' experience of comparing face images for law enforcement and government agencies. Examiners outperformed untrained participants and computer algorithms, thereby providing the first evidence that these examiners are experts at this task. Notably, computationally fusing responses of multiple experts produced near-perfect performance. Results also revealed qualitative differences between expert and non-expert performance. First, examiners' superiority was greatest at longer exposure durations, suggestive of more entailed comparison in forensic examiners. Second, experts were less impaired by image inversion than non-expert students, contrasting with face memory studies that show larger face inversion effects in high performers. We conclude that expertise in matching identity across unfamiliar face images is supported by processes that differ qualitatively from those supporting memory for individual faces.