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.

Description 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. For more details, please visit: https://www.nexdata.ai/datasets/computervision/1134?source=Kaggle

Specifications Data size 5,993 people, 28 images for each person (RGB + IR) Population distribution race distribution: Asian; gender distribution: 3,074 male, 2,919 female; age distribution:ranging from teenager to the elderly, the middle-aged and young people are the majorities Collecting environment indoor scenes, outdoor scenes Data diversity multiple age periods, multiple facial postures, multiple scenes Device Realsense D453i, the resolution is 1,280*720 Data format the image data format is .jpg, the camera parameter information file format is .txt Annotation content label the person – ID, race, gender, age, facial action, collecting scene Accuracy rate based on the accuracy of the actions, the accuracy exceeds 97%; the accuracy of label annotation is not less than 97%

Get the Dataset This is just an example of the data. To access more sample data or request the price, contact us at info@nexdata.ai

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.

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.

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.

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.

Face Detection - Object Detection & Face Recognition Dataset

The dataset is created on the basis of Selfies and ID Dataset

The dataset is a collection of images (selfies) of people and bounding box labeling for their faces. It has been specifically curated for face detection and face recognition tasks. The dataset encompasses diverse demographics, age, ethnicities, and genders.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F01348572e2ae2836f10bc2f2da381009%2FFrame%2050%20(1).png?generation=1699439342545305&alt=media" alt="">

The dataset is a valuable resource for researchers, developers, and organizations working on age prediction and face recognition to train, evaluate, and fine-tune AI models for real-world applications. It can be applied in various domains like psychology, market research, and personalized advertising.

👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset of 95,000+ human images & videos - Full 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_15_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, …, photo_15_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

🧩 This is just an example of the data. Leave a request here to learn more

Dataset structure

• images - contains of original images of people
• labels - includes visualized labeling for the original images
• annotations.xml - contains coordinates of the bbox, created for the original photo

Data Format

Each image from images folder is accompanied by an XML-annotation in the annotations.xml file indicating the coordinates of the polygons and labels . For each point, the x and y coordinates are provided.

Example of XML file structure

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F19e61b2d0780e9db80afe4a0ce879c4b%2Fcarbon.png?generation=1699440100527867&alt=media" alt="">

🚀 You can learn more about our high-quality unique datasets here

keywords: biometric system, biometric system attacks, 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

Introduction

The Middle Eastern Children Facial Image Dataset is a thoughtfully curated collection designed to support the development of advanced facial recognition systems, biometric identity verification, age estimation tools, and child-specific AI models. This dataset enables researchers and developers to build highly accurate, inclusive, and ethically sourced AI solutions for real-world applications.

Facial Image Data

The dataset includes over 1000 high-resolution image sets of children under the age of 18. Each participant contributes approximately 15 unique facial images, captured to reflect natural variations in appearance and context.

Diversity and Representation

Quality and Image Conditions

To ensure robust model training and generalizability, images are captured under varied natural conditions:

Metadata

Each child’s image set is paired with detailed, structured metadata, enabling granular control and filtering during model training:

This metadata is essential for applications that require demographic awareness, such as region-specific facial recognition or bias mitigation in AI models.

Applications

This dataset is ideal for a wide range of computer vision use cases, including:

Ethical Collection and Data Security

We maintain the highest ethical and security standards throughout the data lifecycle:

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.

The Global Facial Recognition Market Size Was Worth $3.86 billion in 2022 and Is Expected To Reach $12.77 billion by the end of 2030, CAGR of 16.10%

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.

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

Face recognition is a biometric technology that identifies or verifies a person's identity by analyzing and comparing facial features from an image or video.This technology offers benefits such as enhanced security in access control, faster and more accurate identity verification, and improved convenience in applications like unlocking devices or streamlining airport check-ins. Additionally, it aids in law enforcement and surveillance, providing tools for crime prevention and public safety.

There are 3 images(fans1 , fans2 , image1) and a video(fansvideo) from football fans which can be used to evaluating face detection models.In addition , there is a Friends Actors images folder which contains All images and Actors folders which in the first one , there are 60 (ten images for each)images of 6 famous actors of Friends serial(Monica - Rachel - Phoebe-Ross - Joey - Chandler) and in the second folder, the actors have split to specific folders with their images .You can also use a video from Friends Serial (namely Friend.mp4 )to check your Recognizor model.

In case you are using SFace Recognition and YUnet Face Detection models , there are 2 ONNX files which one of them is face_detection_yunet_2023mar and the other is face_recognizer_fast.onn that you can use respectively.

background.jpg is just an image for background which additional.

Group photos: (The goal of this data collection was to collect images of groups of people. A group is defined as 2-6 people)

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...

Introduction

Welcome to the Middle Eastern Human Face with Occlusion Dataset, carefully curated to support the development of robust facial recognition systems, occlusion detection models, biometric identification technologies, and KYC verification tools. This dataset provides real-world variability by including facial images with common occlusions, helping AI models perform reliably under challenging conditions.

Facial Image Data

The dataset comprises over 3,000 high-quality facial images, organized into participant-wise sets. Each set includes:

Diversity & Representation

Image Quality & Capture Conditions

To ensure robustness and real-world utility, images were captured under diverse conditions:

Metadata

Each image is paired with detailed metadata to enable advanced filtering, model tuning, and analysis:

This rich metadata helps train models that can recognize faces even when partially obscured.

Use Cases & Applications

This dataset is ideal for a wide range of real-world and research-focused applications, including:

Secure & Ethical Collection

Dataset

This dataset contains 11,113 people with gauze masks, each contributing 7 images, for a total of 77,791 images. The dataset covers multiple mask types, ages, races, light conditions and scenes. This data can be applied to computer vision tasks such as occluded face detection and recognition, masked face recognition and security systems.

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?

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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 insights on th