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.

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.

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.

This Dataset is created by organizing the WIDER FACE dataset. WIDER FACE dataset is a face detection benchmark dataset, of which images are selected from the publicly available WIDER dataset. We chose 32,203 images and labeled 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% of data as training, validation, and testing sets. We adopt the same evaluation metric employed in the PASCAL VOC dataset.

Original Dataset http://shuoyang1213.me/WIDERFACE/

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.

Introduction

Welcome to the Native American Facial Images from Past Dataset, meticulously curated to enhance face recognition models and support the development of advanced biometric identification systems, KYC models, and other facial recognition technologies.

Facial Image Data

This dataset comprises over 5,000+ images, divided into participant-wise sets with each set including:

Diversity and Representation

The dataset includes contributions from a diverse network of individuals across Native American countries:

Quality and Conditions

To ensure high utility and robustness, all images are captured under varying conditions:

Metadata

Each image set is accompanied by detailed metadata for each participant, including:

This metadata is essential for training models that can accurately recognize and identify Native American faces across different demographics and conditions.

Usage and Applications

This facial image dataset is ideal for various applications in the field of computer vision, including but not limited to:

Secure and Ethical Collection

According to Cognitive Market Research, the global Facial Recognition market will be USD 6515.2 million in 2024 and expand at a compound annual growth rate (CAGR) of 17.0% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 2606.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15.2% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 1954.56 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 1498.50 million in 2024 and will grow at a compound annual growth rate (CAGR) of 19.0% from 2024 to 2031.
Latin America's market has more than 5% of the global revenue, with a market size of USD 325.76 million in 2024, and will grow at a compound annual growth rate (CAGR) of 16.4% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 130.30 million in 2024 and will grow at a compound annual growth rate (CAGR) of 16.7% from 2024 to 2031.
The government and defense held the highest facial recognition market revenue share in 2024.

Market Dynamics of Facial Recognition Market

Key Drivers of Facial Recognition Market

Advancements in Technology to Increase the Demand Globally

More advancements in 3D facial recognition and enhanced algorithms make identity recognition more accurate. This increases the technology's dependability for other uses, such as security. The availability of facial recognition software is growing as a cloud-based service. This lowers the barrier to technology adoption for enterprises by removing the need for costly hardware and infrastructure purchases. Artificial intelligence (AI) developments enable facial recognition systems to perform functions beyond simple identification. They can now assess demographics and facial expressions, opening up new possibilities for customer service, marketing, and other fields. The market is expanding because of the increased range of applications for facial recognition that these developments are enabling.

Furthermore, the precision offered by 3D facial recognition systems motivates using these systems for public safety applications, including surveillance and border protection. 3D recognition systems better serve high-security areas such as airports than 2D ones. All of these factors will strengthen the worldwide market.

Increasing Security Concerns to Propel Market Growth

As security concerns grow, facial recognition technology is increasingly employed. This is a key element driving the market for facial recognition technology's growth. People in busy places like train stations, airports, and city centers can be recognized and followed using facial recognition technology. Terrorist acts and criminal activity can both be prevented by this. Travelers' identities can be confirmed via facial recognition, as can the identities of those on watchlists. By doing this, illegal immigration can be stopped, and border security can be strengthened. When someone uses an ATM or other financial facility, facial recognition technology can be used to confirm their identification. Fraud and identity theft may be lessened, and facial recognition can control access to buildings and other secure areas. This can help to prevent unauthorized access and protect sensitive information.

Restraint Factors Of Facial Recognition Marke

Privacy Concerns and Technical Limitations to Limit the Sales

One major obstacle to the widespread application of facial recognition technology is privacy concerns, including the possibility of governments or law enforcement abusing face recognition data. Hacking of facial recognition data could lead to identity theft or unauthorized access to personal data. There is a possibility for widespread monitoring and tracking of individuals without their knowledge or agreement through mass surveillance. The use of facial recognition technology is now subject to certain laws and limitations as a result of privacy concerns. For instance, the General Data Protection Regulation (GDPR) in Europe imposes stringent restrictions on the collection and use of face recognition data, and several American towns have outlawed the use of facial recognition technology by law enforcement. The future of the facial recognition market is unclear. ...

Includes videos of 11 subjects, each showing 18 different angles of their face for one second each. The process was repeated with 5 light settings (warm, cold, low, medium, and bright). Videos are recorded in 3840 pixels tall by 2160 pixels wide and are saved in .MP4 format.

Love And Hip Hop Facial Recognition

## Overview

Love And Hip Hop Facial Recognition is a dataset for object detection tasks - it contains Faces annotations for 430 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).

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.

Introduction

Welcome to the East Asian Facial Images from Past Dataset, meticulously curated to enhance face recognition models and support the development of advanced biometric identification systems, KYC models, and other facial recognition technologies.

Facial Image Data

This dataset comprises over 10,000+ images, divided into participant-wise sets with each set including:

Diversity and Representation

The dataset includes contributions from a diverse network of individuals across East Asian countries:

Quality and Conditions

To ensure high utility and robustness, all images are captured under varying conditions:

Metadata

Each image set is accompanied by detailed metadata for each participant, including:

This metadata is essential for training models that can accurately recognize and identify East Asian faces across different demographics and conditions.

Usage and Applications

This facial image dataset is ideal for various applications in the field of computer vision, including but not limited to:

Secure and Ethical Collection

Driver Face Detection

## Overview

Driver Face Detection is a dataset for object detection tasks - it contains Distracted annotations for 3,406 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).

Snapshot img

Facial Recognition Market Size 2024-2028

The facial recognition market size is forecast to increase by USD 11.82 billion, at a CAGR of 22.2% between 2023 and 2028.

The market landscape is experiencing substantial growth, leading to a significant increase in demand for advanced identity verification. Organizations are prioritizing security measures, resulting in a rising need for precise and efficient identity verification processes. Key market trends include technological advancements and the emergence of facial analytics, which enhance accuracy and efficiency.
However, the high cost of deployment remains a significant challenge, potentially limiting access for smaller businesses and organizations. Overcoming this hurdle is essential for fostering broader adoption of digital identity and security and ensuring sustained growth in the market, particularly in the coming years.
The facial recognition market is expanding, driven by AI facial recognition and biometric authentication technologies. These advancements support security surveillance, contactless identity verification, and emotion detection technology. Cloud-based facial recognition systems leverage video analytics for enhanced public safety applications and access control solutions. However, privacy regulations play a significant role in shaping market growth, ensuring secure and compliant implementation of these systems in various sectors.

What will be the Size of the Facial Recognition Market During the Forecast Period?

To learn more about the facial recognition market report, Request Free Sample

Facial recognition technology is widely used across sectors like education for attendance, healthcare for patient monitoring, and retail for access control. Biometric POS Terminals integrate facial recognition to enhance payment security and efficiency. This technology also supports banking and law enforcement with secure authentication and surveillance.
Companies and technology corporations are pioneering advancements in facial recognition and biometric access control systems, employing technologies like image recognition and speech recognition. Facial characteristics, including jawline and facial contours, are analyzed to authenticate individuals. The application of facial recognition technology extends to smart hospitality services, enhancing the overall customer experience. This technology offers enhanced security and efficiency across multiple industries.

How is the Facial Recognition Market Segmented?

The facial recognition market trends and analysis report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion ' for the period 2024-2028, as well as historical data from 2018 - 2022 for the following segments.

Application Outlook 

  Identification
  Verification


Technology Outlook 

  3D
  2D
  Facial analytics


End-user Outlook 

  Media and entertainment
  BFSI
  Automobile and transportation
  Others


Region Outlook 

  North America

    The U.S.
    Canada





  Europe

    The U.K.
    Germany
    France
    Rest of Europe





  APAC

    China
    India


  South America

    Chile
    Argentina
    Brazil





  Middle East & Africa

    Saudi Arabia
    South Africa
    Rest of the Middle East & Africa

By Application

The market share growth by the identification segment will be significant during the forecast period. Facial recognition technology has emerged as a significant solution for identification and verification in various sectors. NEC Corporation, Microsoft, AWS, and other tech giants are leading the market with advanced facial recognition systems. KYC systems and digital payments are integrating facial recognition for secure authentication. Smartphone applications and physical security systems also utilize this technology for access control and surveillance.

Get a glance at the market share of various regions. Download the PDF Sample

The identification segment was valued at USD 3.04 billion in 2018. Facial recognition systems use facial features, such as jawline and unique identifiers, to authenticate individuals. These systems are widely adopted in public safety and physical security for identification and verification purposes. The transportation sector, particularly airports, has seen a significant increase in the adoption of facial recognition technology for entry/exit systems.
Sectors requiring strict access control and video surveillance, such as banking and law enforcement, are increasingly relying on facial recognition technology for identification and verification. Authentication techniques using facial recognition are more secure and efficient compared to traditional methods. The global market for facial recognition technology is expected to grow significantly due to its wide adoption in various sectors.

Regional Analysis

For more insi

Facial Recognition Market size was valued at USD 6.15 Billion in 2024 and is projected to reach USD 14.62 Billion by 2031, growing at a CAGR of 12.62% from 2024 to 2031.

Facial Recognition Market Drivers

Security and Surveillance: Facial recognition is used for security and surveillance purposes, such as access control, law enforcement, and crowd management.
Payment and Authentication: Facial recognition is being adopted for biometric authentication, enabling secure payments and logins.
Consumer Electronics: Facial recognition is integrated into smartphones, laptops, and other devices for unlocking, authentication, and facial recognition apps.

Facial Recognition Market Restraints

Privacy Concerns: The use of facial recognition raises privacy concerns, as it involves collecting and storing biometric data.
Accuracy and Bias: Facial recognition systems may not be accurate for all individuals, especially those with darker skin tones or facial features that are not well-represented in training data.
Regulatory Challenges: Governments are implementing regulations to address privacy concerns and ensure ethical use of facial recognition technology.

Real Time Facial Recognition

## Overview

Real Time Facial Recognition is a dataset for object detection tasks - it contains Face annotations for 555 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).

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.

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.

Introduction

Welcome to the Middle Eastern Child Faces Dataset, meticulously curated to enhance face recognition models and support the development of advanced biometric identification systems, child identification models, and other facial recognition technologies.

Facial Image Data

This dataset comprises over 3,000 child image sets, divided into participant-wise sets with each set including:

Diversity and Representation

The dataset includes contributions from a diverse network of children across Middle Eastern countries:

Quality and Conditions

To ensure high utility and robustness, all images are captured under varying conditions:

Metadata

Each facial image set is accompanied by detailed metadata for each participant, including:

This metadata is essential for training models that can accurately recognize and identify children's faces across different demographics and conditions.

Usage and Applications

This facial image dataset is ideal for various applications in the field of computer vision, including but not limited to:

Secure and Ethical Collection

5,993 People – Infrared Face Recognition Data. The collecting scenes of this dataset include indoor scenes and outdoor scenes. The data includes male and female. The age distribution ranges from child to the elderly, the young people and the middle aged are the majorities. The collecting device is realsense D453i. The data diversity includes multiple age periods, multiple facial postures, multiple scenes. The data can be used for tasks such as infrared face recognition.

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">