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.

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.

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, while the second file, named "yolo_format_labels," contains annotations in YOLO format. The dataset was obtained by employing the OIDv4 toolkit, specifically designed for scraping data from Google Open Images. Notably, this dataset exclusively focuses on face detection.

This dataset offers a highly suitable resource for training deep learning models specifically designed for face detection tasks. The images within the dataset exhibit exceptional quality and have been meticulously annotated with bounding boxes encompassing the facial regions. The annotations are provided in two formats: the original scale, denoting the pixel coordinates of the bounding boxes, and the YOLO format, representing the bounding box coordinates in normalized form.

The dataset was meticulously curated by scraping relevant images from Google Open Images through the use of the OIDv4 toolkit. Only images that are pertinent to face detection tasks have been included in this dataset. Consequently, it serves as an ideal choice for training deep learning models that specifically target face detection tasks.

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.

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.

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:

Data Collection - TagX can provides the dataset based on following scenarios to train a biasfree face analysis and detection dataset- Single and multiple faces images Monk skin-tones covered All Facial angles included

Metadata for Face Images- We can provide following metadata along with the collected images Age Range Distance from camera Emotion State Accessories present(Eyeglasses, hat etc.) pose with the values of pitch, roll, and yaw.

Annotation of Face Images- We can provides annotation for face detection applications like Bounding box annotation, Landmark annotation or polygon annotation. We have a dataset prepared with bounding box annotation around faces for 30000 images.

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.

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.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1746215%2F92752ca2b0bbecdd3fd154b88495558d%2F1_RaupR7k7NrrTJZvop7sH-A.png?generation=1573849119616339&alt=media" alt="LFW-PEOPLE">

This dataset is a collection of JPEG pictures of famous people collected on the internet. All details are available on the official website: http://vis-www.cs.umass.edu/lfw/

Each picture is centered on a single face. Each pixel of each channel (color in RGB) is encoded by a float in range 0.0 - 1.0.

The task is called Face Recognition (or Identification): given the picture of a face, find the name of the person given a training set (gallery).

The original images are 250 x 250 pixels, but the default slice and resize arguments reduce them to 62 x 47 pixels.

Acknowledgements

We wouldn't be here without the help of others. I would like to thank Computer Vision Laboratory, university of Massachusetts for providing us with such an excellent database.

Inspiration

I had an activity in my college for facial recognition. I came up with this as the best kind of dataset for my task. I am posting it here on Kaggle to make it available for other data scientists conveniently and see what magic they can perform with this amazing dataset.

From the website

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 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.

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

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.

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%

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

Tufts-Face-Database

Multi-modal face images (112 participants, >100,000 images in total)

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 10,000 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 array of 4 night vision cameras). The l...

Introduction

Welcome to the Hispanic Human Facial Images Dataset, curated to advance facial recognition technology and support the development of secure biometric identity systems, KYC verification processes, and AI-driven computer vision applications. This dataset is designed to serve as a robust foundation for real-world face matching and recognition use cases.

Facial Image Data

The dataset contains over 1500 facial image sets of Hispanic individuals. Each set includes:

Diversity & Representation

Image Quality & Capture Conditions

All images were captured with real-world variability to enhance dataset robustness:

Metadata

Each participant’s data is accompanied by rich metadata to support AI model training, including:

This metadata enables targeted filtering and training across diverse scenarios.

Use Cases & Applications

This dataset is ideal for a wide range of AI and biometric applications:

Secure & Ethical Collection

Dataset Updates & Customization

To meet evolving AI demands, this dataset is regularly updated and can be customized. Available options include:

The unavailability of a unified standard dataset for face mask detection and masked facial recognition motivated us to develop an in-house MDMFR dataset (MDMFR, 2022) to measure the performance of face mask detection and masked facial recognition methods. Both of these tasks have different dataset requirements. Face mask detection requires the images of multiple persons with and without mask. Whereas, masked face recognition requires multiple masked face images of the same person. Our MDMFR dataset consists of two main collections, 1) face mask detection, and 2) masked facial recognition. There are 6006 images in our MDMFR dataset. The face mask detection collection contains two categories of face images i.e., mask and unmask. Our detection database consists of 3174 with mask and 2832 without mask (unmasked) images. To construct the dataset, we captured multiple images of the same person in two configurations (mask and without mask). The masked facial recognition collection contains a total of 2896 masked images of 226 persons. More specifically, our dataset includes the images of both male and female persons of all ages including the children. The images of our dataset are diverse in terms of gender, race, and age of users, types of masks, illumination conditions, face angles, occlusions, environment, format, dimensions, and size, etc. Before being fed to our DeepMaskNet model, all images are scaled to a width and height of 256 pixels. All images have a bit depth of 24. We prepared the images of our dataset for the proposed DeepMaskNet model during preprocessing where images are cropped in Adobe-Photoshop to exclude the extra information like neck and shoulder. As the input size of our Deepmasknet model was 256-by-256, so images were resized to 256-by-256 in publicly available Plastiliq Image Resizer software (Plastiliq, 2022).

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.

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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

While face recognition techniques have achieved remarkable performance in real- world applications, important issues still need to be addressed. Gender and race bias, as well as identity privacy problems, are among the top concerns due to their significant societal impact. Gender and race bias result in unequal accuracy between genders and across races. The identity privacy problem is related to the collection of training sets, as these sets are typically gathered without obtaining permission from the individuals represented in the dataset.

Our previous work has shown that facial attributes, such as facial hair, hairstyle, and face exposure, can significantly affect face recognition performance. We demon- strate that bias can be largely mitigated by balancing the distribution of these at- tributes in both the training set and the test set. The privacy problem has been exacerbated by government regulations (e.g., the General Data Privacy Regulation, or GDPR), which protect identity privacy but also hinder the development of more powerful face recognition techniques.

To address these problems, this proposed research aims to design a controlled face image generation model that can create images of non-existent identities to form a synthetic training set while controlling attribute distributions. After this, we notice that only pose and age variations are included in the test sets, which is insufficient to measure the intra-class variation of the generated training sets. To this end, we propose three test sets that focus on additional two attribute variations and identical twins. Lastly, we unlock the attribute control of the proposed model and conduct a comprehensive analysis to reveal the weaknesses of the existing synthetic face recognition datasets and provide insights for future work in this area.