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.

The Tufts Face Database is a comprehensive collection of human face images, ideal for facial recognition, biometric verification, and computer vision model training. It includes diverse data by ethnicity, age, gender, and region for robust AI development.

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.

Origin

Originally obtained from the Yale Face Database.

Content

The database contains 165 GIF images of 15 subjects (subject01, subject02, etc.).

There are 11 images per subject, one for each of the following facial expressions or configurations: center-light, w/glasses, happy, left-light, w/no glasses, normal, right-light, sad, sleepy, surprised, and wink.

Note that the image "subject04.sad" has been corrupted and has been substituted by "subject04.normal".

Acknowledgments

It is free to use the data for research purposes. If experimental results are obtained that use images from within the database, all publications of these results should acknowledge the use of the "Yale Face Database". Without permission from Yale, images from within the database cannot be incorporated into a larger database which is then publicly distributed.

Inspiration

This data is very useful for starting experiments in face 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.

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.

Unlock the potential of AI-driven face recognition systems with this comprehensive dataset designed to fuel innovation and advancements in facial recognition technology. Featuring a diverse collection of facial images meticulously curated from various sources, including public databases, social media platforms, and research datasets, this dataset offers a rich repository for training and testing face recognition algorithms. Each image is labeled with metadata, including gender, age, ethnicity, and pose, facilitating detailed analysis and benchmarking of facial recognition models. Researchers, developers, and enthusiasts alike can explore this dataset to develop robust algorithms, evaluate performance metrics, and address ethical considerations in facial recognition technology. Whether you're working on improving accuracy, enhancing privacy measures, or exploring novel applications, this dataset provides a solid foundation for pushing the boundaries of AI-powered face recognition systems. Unlock the potential of facial data and embark on a journey towards more secure, inclusive, and ethically-driven facial recognition solutions.

8.5 gigabytes of faces for training facial recognition software.

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.

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.

Biometric Data

FileMarket provides a comprehensive Biometric Data set, ideal for enhancing AI applications in security, identity verification, and more. In addition to Biometric Data, we offer specialized datasets across Object Detection Data, Machine Learning (ML) Data, Large Language Model (LLM) Data, and Deep Learning (DL) Data. Each dataset is meticulously crafted to support the development of cutting-edge AI models.

Data Size: 20,000 IDs

Race Distribution: The dataset encompasses individuals from diverse racial backgrounds, including Black, Caucasian, Indian, and Asian groups.

Gender Distribution: The dataset equally represents all genders, ensuring a balanced and inclusive collection.

Age Distribution: The data spans a broad age range, including young, middle-aged, and senior individuals, providing comprehensive age coverage.

Collection Environment: Data has been gathered in both indoor and outdoor environments, ensuring variety and relevance for real-world applications.

Data Diversity: This dataset includes a rich variety of face poses, racial backgrounds, age groups, lighting conditions, and scenes, making it ideal for robust biometric model training.

Device: All data has been collected using mobile phones, reflecting common real-world usage scenarios.

Data Format: The data is provided in .jpg and .png formats, ensuring compatibility with various processing tools and systems.

Accuracy: The labels for face pose, race, gender, and age are highly accurate, exceeding 95%, making this dataset reliable for training high-performance biometric models.

Tufts-Face-Database

Multi-modal face images (113 participants, >100,000 images; 1 retracted, resulting in 112 participants)

7 image modalities: visible, near-infrared, thermal, computerized sketch, video, LYTRO and 3D images

Context

Tufts Face Database is the most comprehensive, large-scale (over 100,000 images, 74 females + 38 males, from more than 15 countries with an age range between 4 to 70 years old) face dataset that contains 7 image modalities: visible, near-infrared, thermal, computerized sketch, LYTRO, recorded video, and 3D images. This webpage/dataset contains the Tufts Face Database three-dimensional (3D) images. The other datasets are made available through separate links by the user.

Cross-modality face recognition is an emerging topic due to the wide-spread usage of different sensors in day-to-day life applications. The development of face recognition systems relies greatly on existing databases for evaluation and obtaining training examples for data-hungry machine learning algorithms. However, currently, there is no publicly available face database that includes more than two modalities for the same subject. In this work, we introduce the Tufts Face Database that includes images acquired in various modalities: photograph images, thermal images, near infrared images, a recorded video, a computerized facial sketch, and 3D images of each volunteer’s face. An Institutional Research Board protocol was obtained, and images were collected from students, staff, faculty, and their family members at Tufts University.

This database will be available to researchers worldwide in order to benchmark facial recognition algorithms for sketch, thermal, NIR, 3D face recognition and heterogamous face recognition.

Links to modalities of the Tufts Face Database

1. Tufts Face Database Computerized Sketches (TD_CS)

2. Tufts Face Database Thermal (TD_IR) Around+Emotion

3. Tufts Face Database Thermal Cropped (TD_IR_Cropped) Emotion only

4. Tufts Face Database Three Dimensional (3D) (TD_3D)

5. Tufts Face Database Lytro (TD_LYT) (Check Note)

6. Tufts Face Database 2D RGB Around (TD_RGB_A) (Check Note)

7. Tufts Face Database 2D RGB Emotion (TD_RGB_E) (Check Note)

8. Tufts Face Database Night Vision (NIR) (TD_NIR) (Check Note)

9. Tufts Face Database Video (TD_VIDEO) (Check Note)

10. Tufts Face Thermal2RGB Dataset

Note: Please use http instead of https. The link appears broken when https is used.

Image Acquisition

Each participant was seated in front of a blue background in close proximity to the camera. The cameras were mounted on tripods and the height of each camera was adjusted manually to correspond to the image center. The distance to the participant was strictly controlled during the acquisition process. A constant lighting condition was maintained using diffused lights.

TD_CS: Computerized facial sketches were generated using software FACES 4.0 [1], one of the most widely used software packages by law enforcement agencies, the FBI, and the US Military. The software allows researchers to choose a set of candidate facial components from the database based on their observation or memory.

TD_3D: The images were captured using a quad camera (an array of 4 cameras). Each individual was asked to look at a fixed view-point while the cameras were moved to 9 equidistant positions forming an approximate semi-circle around the individual. The 3D models were reconstructed using open-source structure-from-motion algorithms.

TD_IR_E(E stands for expression/emotion): The images were captured using a FLIR Vue Pro camera. Each participant was asked to pose with (1) a neutral expression, (2) a smile, (3) eyes closed, (4) exaggerated shocked expression, (5) sunglasses.

TD_IR_A (A stands for around): The images were captured using a FLIR Vue Pro camera. Each participant was asked to look at a fixed view-point while the cameras were moved to 9 equidistant positions forming an approximate semi-circle around the participant .

TD_RGB_E: The images were captured using a NIKON D3100 camera. Each participant was asked to pose with (1) a neutral expression, (2) a smile, (3) eyes closed, (4) exaggerated shocked expression, (5) sunglasses.

TD_RGB_A: The images were captured using a quad camera (an array of 4 visible field cameras). Each participant was asked to look at a fixed view-point while the cameras were moved to 9 equidistant positions forming an approximate semi-circle around the participant.

TD_NIR_A: The images were captured using a quad camera (an arra...

4,458 People - 3D Facial Expressions Recognition Data. The collection scenes include indoor scenes and outdoor scenes. The dataset includes males and females. The age distribution ranges from juvenile to the elderly, the young people and the middle aged are the majorities. The device includes iPhone X, iPhone XR. The data diversity includes different expressions, different ages, different races, different collecting scenes. This data can be used for tasks such as 3D facial expression recognition.

Data size 4,458 people, 7 kinds of 3D expressions were collected for each person

Population distribution race distribution: Asian, Black, Caucasian; gender distribution: male, female; age distribution: ranging from teenager to the elderly, the middle-aged and young people are the majorities

Collecting environment including indoor and outdoor scenes

Data diversity different expressions, different ages, different races, different collecting scenes

Device iPhone X, iPhone XR

Data format .jpg, .xml, .json

Annotation content label the person – ID, race, gender, age, expression action, collecting scene

Accuracy based on the accuracy of the actions, the accuracy exceeds 97%; the accuracy of labels is not less than 97%

snapshot-tab-pane Facial Recognition Market Size 2026-2030The facial recognition market size is valued to increase by USD 20.37 billion, at a CAGR of 25.5% from 2025 to 2030. Increasing instances of identity threats will drive the facial recognition market.Major Market Trends & InsightsEurope dominated the market and accounted for a 38.5% growth during the forecast period.By Application - Identification segment was valued at USD 5.83 billion in 2024By Technology - 3D segment accounted for the largest market revenue share in 2024Market Size & ForecastMarket Opportunities: USD 25.61 billionMarket Future Opportunities: USD 20.37 billionCAGR from 2025 to 2030 : 25.5%Market SummaryThe facial recognition market is evolving beyond simple surveillance, driven by the need for secure and frictionless authentication. The adoption of 3D facial recognition and advanced liveness detection techniques is becoming standard to combat sophisticated spoofing attacks.A key trend is the integration of multimodal biometric systems, which combine facial scans with other identifiers like iris or fingerprint recognition for layered security. For instance, a financial institution can deploy this for remote client onboarding, using one-to-one matching for identity verification against a government-issued ID while also performing a liveness check to prevent fraud.This significantly enhances security over traditional methods. However, the industry grapples with challenges related to biometric data privacy and the high costs of implementation. The development of robust deep learning algorithms continues to improve accuracy and mitigate demographic bias, but regulatory scrutiny and public perception remain critical factors shaping the market's trajectory and acceptance in various applications.What will be the Size of the Facial Recognition Market during the forecast period? Get Key Insights on Market Forecast (PDF) Request Free SampleHow is the Facial Recognition Market Segmented?The facial recognition industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2026-2030, as well as historical data from 2020-2024 for the following segments.ApplicationIdentificationVerificationTechnology3D2DFacial analyticsEnd-userMedia and entertainmentBFSIAutomobile and transportationOthersGeographyNorth AmericaUSCanadaMexicoEuropeGermanyUKFranceAPACChinaIndiaJapanMiddle East and AfricaUAESaudi ArabiaSouth AfricaSouth AmericaBrazilArgentinaRest of World (ROW)By Application InsightsThe identification segment is estimated to witness significant growth during the forecast period.The identification segment is defined by its one-to-many matching process, crucial for public safety initiatives and law enforcement surveillance. This technology, powered by deep learning algorithms, is integral to smart city projects for public management and combating organized retail crime.It processes vast video surveillance integration feeds to find individuals in large datasets, a task where 2d facial recognition is often employed for its scalability.Forensic analysis tools utilize these capabilities, including age estimation and gender recognition, to narrow down suspects.Continual advancements have reduced the false rejection rate by over 15%, enhancing system reliability for security teams who depend on automated alerts for threat detection and proactive intervention within complex urban environments. Request Free SampleThe Identification segment was valued at USD 5.83 billion in 2024 and showed a gradual increase during the forecast period. Request Free SampleRegional AnalysisEurope is estimated to contribute 38.5% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. See How Facial Recognition Market Demand is Rising in Europe Request Free SampleThe geographic landscape of the facial recognition market is diverse, with regional priorities shaping deployment. North America focuses on national security protocols and border control management, contributing 35.26% of the growth opportunity.Europe, contributing 38.53%, navigates a stricter regulatory environment, emphasizing privacy-preserving biometric software technology. In contrast, APAC demonstrates rapid adoption, driven by government-led smart city initiatives and the integration of automated biometric identification systems.Technological advancements, such as edge computing biometrics and cross-platform face recognition SDKs, enable tailored solutions for regional needs, from driver monitoring systems enhancing automotive safety with driver alertness monitoring and in-cabin personalization to biom

10,543 People - Face Recognition Data at Ticket Gate, for each subject, 4 images were collected. The dataset diversity includes different shooting heights, different ages, different light conditions and scenes.This data can be applied to computer vision tasks such as face detection and recognition.

Find 3+ verified Facial Recognition founder emails. Access decision makers, CEOs, and CTOs in Facial Recognition companies for B2B sales & recruiting.

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 detection algorithms for more accurate recognizing faces in real-world scenarios. - Get the data

Metadata for the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F87acb75b060abcd7838e8a9fad21fb79%2FFrame%201%20(8).png?generation=1743153407873743&alt=media" alt=""> All images come with rigorously verified metadata annotations (age, gender, ethnicity), achieving ≥95% labeling accuracy. Also images are captured under different lighting conditions and resolutions, enhancing the dataset's utility for computer vision tasks and image classifications.

💵 Buy the Dataset: This is a limited preview of the data. To access the full dataset, please contact us at https://unidata.pro to discuss your requirements and pricing options.

Researchers can leverage this dataset to improve recognition technology and develop learning models that enhance the accuracy of face detections. The dataset also supports projects focused on face anti-spoofing and deep learning applications, making it an essential tool for those studying biometric security and liveness detection technologies.

🌐 UniData provides high-quality datasets, content moderation, data collection and annotation for your AI/ML projects

According to Cognitive Market Research, the global Facial Recognition market was 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. Alth...

The collecting scenes of this dataset include indoor scenes and outdoor scenes. The data includes male and female. The race distribution includes Asian, Black, Caucasian and Brown people. The age distribution ranges from children to elderly. The collecting device is DV-DH4,044S305AD. The data diversity includes multiple age periods, multiple facial postures, multiple scenes. The data can be used for tasks such as AI-based infrared facial recognition and biometric authentication. We strictly adhere to data protection regulations and privacy standards, ensuring the maintenance of user privacy and legal rights throughout the data collection, storage, and usage processes, our datasets are all GDPR, CCPA, PIPL complied.

Data size 4,484 people, 28 images for each person (RGB + IR)

Population distribution race distribution: 2,503 Asians, 565 blacks, 683 Caucasians, 733 brown people; gender distribution:2,813 males, 1,671 females; age distribution: ranging from teenager to the elderly, the middle-aged and young people are the majorities

Collecting environment there were 3,561 people in indoor scenes and 923 people in outdoor scenes

Data diversity multiple age periods, multiple facial postures, multiple scenes

Device DV-DH4,044S305AD, the resolution is 1,9201,080

Data format the image data format is .jpg, the camera parameter information file format is .txt

Annotation content label the person – ID, nationality, gender, age, facial action, collecting scene

Accuracy rate label the person – ID, nationality, gender, age, facial action, collecting scene; the accuracy of label annotation is not less than 97%

The Face Recognition Access Control All-in-one Machine market is booming, projected to reach $7.885 billion by 2033 with a 15% CAGR. Learn about key drivers, trends, restraints, and top companies shaping this rapidly growing sector. Explore market segmentation, regional analysis, and future projections in our in-depth market report.