Explore the Sokoto Coventry Fingerprint Dataset (SOCOFing), a robust biometric database featuring 6,000 fingerprint images from 600 African subjects. Ideal for academic research, SOCOFing includes detailed labels and synthetic alterations to enhance fingerprint recognition and security studies.

A DATASET CONTAINING FINGERPRINT OF 100 FAMILY OF WEST BENGAL INDIA. THE DATASET CONTAINS FINGERPRINT IMAGE OF FIVE FINGER OF RIGHT HAND OF FATHER, MOTHER AND CHILD. THE DATASET CONTAINS TOTAL 1500 FINGERPRINT IMAGE IN PNG FORMAT. THE SIZE OF THE FINGERPRINT IMAGES ARE 512 x 512 PIXELS.

2D and 3D Fingerrpint dataset. It was created by former Ph.D. stduent Ms. Wei Zhou under the supervision of Prof. J. Hu who acts as the communication contact.

NIST, working with the FBI, has digitized 888 inked fingerprint arrest cards that were in various physical conditions, from pristine to badly damaged and faded, and were collected during law enforcement professionals' duties. This database contains images of the 10 rolled fingerprint impressions, the two four-finger slap impressions (finger positions 13 and 14), the two thumb slap impressions (finger positions 11 and 12) and the segmented impressions from the slap images (13,14). The database also includes the coordinates that were used to segment the impressions from the slap fingerprint images.The cards were scanned at three different resolutions: 500, 1,000, and 2,000 pixels per inch (PPI). All three resolutions were scanned in grayscale at a depth of 8 bits-per pixel.Data available as of July 2018 is Special Database 300a, in 500 ppi with PNG formatted impressions. Data at other resolutions, in other image formats, and in other record types may be forthcoming.

In April 2017, the Intelligence Advanced Research Projects Activity (IARPA) held a dry run for the data collection portion of its Nail to Nail (N2N) Fingerprint Challenge. This data collection event was designed to ensure that the real data collection event held in September 2017 would be successful. To this end, biometric data from unhabituated individuals needed to be collected. That data is now released by NIST as Special Database 301.In total, 14 fingerprint sensors were deployed during the data collection, amassing a series of rolled and plain images. The devices include rolled fingerprints captured by skilled experts from the Federal Bureau of Investigation (FBI) Biometric Training Team. Captures of slaps, palms, and other plain impression fingerprint impressions were additionally recorded. NIST also partnered with the FBI and Schwarz Forensic Enterprises to design activity scenarios in which subjects would likely leave fingerprints on different objects. The activities and associated objects were chosen in order to use a number of latent print development techniques and simulate the types of objects often found in real law enforcement case work. NIST also collected some mugshot-style face and iris images of the subjects who participated in the dry run. These data are also available for download.

Fingerprint Pattern Detection

## Overview

Fingerprint Pattern Detection is a dataset for object detection tasks - it contains Fingerprints Fingerprints 4pZv annotations for 1,730 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).

and their augmented replicas.

Fingerprint

## Overview

Fingerprint is a dataset for object detection tasks - it contains Index Little Middle Ring annotations for 538 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 LUTBIO database provides a comprehensive resource for research in multimodal biometric authentication, featuring the following key aspects:

• Extensive Biometric Modalities: The database contains data from nine biometric modalities: voice, face, fingerprint, contact-based palmprint, electrocardiogram (ECG), opisthenar (back of hand), ear, contactless palmprint, and periocular region.

• Diverse Demographics: Data were collected from 306 individuals, with a balanced gender distribution of 164 males and 142 females, spanning an age range of 8 to 90 years. This diverse age representation enables analyses across a wide demographic spectrum.

• Representative Population Sampling: Volunteers were recruited from naturally occurring communities, ensuring a large-scale, statistically representative population. The collected data encompass variations observed in real-world environments.

• Support for Multimodal and Cross-Modality Research: LUTBIO provides both contact-based and contactless palmprint data, as well as fingerprint data (from optical images and scans), promoting advancements in multimodal biometric authentication. This resource is designed to guide the development of future multimodal databases.

• Flexible, Decouplable Data: The biometric data in the LUTBIO database are designed to be highly decouplable, enabling independent processing of each modality without loss of information. This flexibility supports both single-modality and multimodal analysis, empowering researchers to optimize, combine, and customize biometric features for specific applications.

✅ Data Availability: If you wish to use the LUTBIO dataset, please download the attached Word document, fill in the information, and send it as an attachment to rykeryang AT 163.com. We will process your request as soon as possible!

🥸 Important Notice: Please read the data collection protocol of the LUTBIO dataset carefully before use, as it is essential for understanding and correctly interpreting the dataset. Thank you.

😎 Good news! Our paper has been accepted by Information Fusion, and the DOI is https://doi.org/10.1016/j.inffus.2025.102945. We appreciate the reviewers and the editor for their efforts.🥰🥰🥰

In September 2017, the Intelligence Advanced Research Projects Activity (IARPA) held a data collection as part of its Nail to Nail (N2N) Fingerprint Challenge. Participating Challengers deployed devices designed to collect an image of the full nail to nail surface area of a fingerprint equivalent to a rolled fingerprint from an unacclimated user, without assistance from a trained operator. Traditional operator-assisted live-scan rolled fingerprints were also captured, along with assorted other friction ridge live-scan and latent captures.In this data collection, study participants needed to have their fingerprints captured using traditional operator-assisted techniques in order to quantify the performance of the Challenger devices. IARPA invited members of the Federal Bureau of Investigation (FBI) Biometric Training Team to the data collection to perform this task. Each study participant had N2N fingerprint images captured twice, each by a different FBI expert, resulting in two N2N baseline datasets.To ensure the veracity of recorded N2N finger positions in the baseline datasets, Challenge test staff also captured plain fingerprint impressions in a 4-4-2 slap configuration. This capture method refers to simultaneously imaging the index, middle, ring, and little fingers on the right hand, then repeating the process on the left hand, and finishing with the simultaneous capture of the left and right thumbs. This technique is a best practice to ensure finger sequence order, since it is physically challenging for a study participant to change the ordering of fingers when imaging them simultaneously. There were four baseline (two rolled and two slap), eight challenger and ten auxiliary fingerprint sensors deployed during the data collection, amassing a series of rolled and plain images. It was required that the baseline devices achieve 100% acquisition rate, in order to verify the recorded friction ridge generalized positions (FRGPs) and study participant identifiers for other devices. There were no such requirements for Challenger devices. Not all devices were able to achieve 100% acquisition rate.Plain, rolled, and touch-free impression fingerprints were captured from a multitude of devices, as well as sets of plain palm impressions. NIST also partnered with the FBI and Schwarz Forensic Enterprises (SFE) to design activity scenarios in which subjects would likely leave fingerprints on different objects. The activities and associated objects were chosen in order to use a number of latent print development techniques and simulate the types of objects often found in real law enforcement case work.

Fingerprint Minutiae Detection

## Overview

Fingerprint Minutiae Detection is a dataset for object detection tasks - it contains BIfurcation annotations for 300 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 NIST database of fingerprint images contains 2000 8-bit gray scale fingerprint image pairs. Each image is 512-by-512 pixels with 32 rows of white space at the bottom and classified using one of the five following classes:

Gender Detection & Classification - face recognition dataset

The dataset is created on the basis of Face Mask Detection dataset

Dataset Description:

The dataset comprises a collection of photos of people, organized into folders labeled "women" and "men." Each folder contains a significant number of images to facilitate training and testing of gender detection algorithms or models.

The dataset contains a variety of images capturing female and male individuals from diverse backgrounds, age groups, and ethnicities.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F1c4708f0b856f7889e3c0eea434fe8e2%2FFrame%2045%20(1).png?generation=1698764294000412&alt=media" alt="">

This labeled dataset can be utilized as training data for machine learning models, computer vision applications, and gender detection algorithms.

💴 For Commercial Usage: Full version of the dataset includes 376 000+ photos of people, leave a request on TrainingData to buy the 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_3_extension, photo_4_extension - photo extensions in the dataset
• photo_1_resolution, photo_2_resolution, photo_3_extension, photo_4_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

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to learn about the price and buy the dataset

Content

The dataset is split into train and test folders, each folder includes: - folders women and men - folders with images of people with the corresponding gender, - .csv file - contains information about the images and people in the dataset

File with the extension .csv

• file: link to access the file,
• gender: gender of a person in the photo (woman/man),
• split: classification on train and test

TrainingData provides high-quality data annotation tailored to your needs

keywords: biometric system, biometric system attacks, biometric dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, gender detection, supervised learning dataset, gender classification dataset, gender recognition dataset

The BED dataset

Version 1.0.0

Please cite as: Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12219 - 12230, 2021.

Disclaimer

While every care has been taken to ensure the accuracy of the data included in the BED dataset, the authors and the University of the West of Scotland, Durham University, and Universitat de València do not provide any guaranties and disclaim all responsibility and all liability (including without limitation, liability in negligence) for all expenses, losses, damages (including indirect or consequential damage) and costs which you might incur as a result of the provided data being inaccurate or incomplete in any way and for any reason. 2020, University of the West of Scotland, Scotland, United Kingdom.

Contact

For inquiries regarding the BED dataset, please contact:

1. Dr Pablo Arnau-González, arnau.pablo [*AT*] gmail.com
2. Dr Stamos Katsigiannis, stamos.katsigiannis [*AT*] durham.ac.uk
3. Prof. Miguel Arevalillo-Herráez, miguel.arevalillo [*AT*] uv.es
4. Prof. Naeem Ramzan, Naeem.Ramzan [*AT*] uws.ac.uk

Dataset summary

BED (Biometric EEG Dataset) is a dataset specifically designed to test EEG-based biometric approaches that use relatively inexpensive consumer-grade devices, more specifically the Emotiv EPOC+ in this case. This dataset includes EEG responses from 21 subjects to 12 different stimuli, across 3 different chronologically disjointed sessions. We have also considered stimuli aimed to elicit different affective states, so as to facilitate future research on the influence of emotions on EEG-based biometric tasks. In addition, we provide a baseline performance analysis to outline the potential of consumer-grade EEG devices for subject identification and verification. It must be noted that, in this work, EEG data were acquired in a controlled environment in order to reduce the variability in the acquired data stemming from external conditions.

The stimuli include:

• Images selected to elicit specific emotions
• Mathematical computations (2-digit additions)
• Resting-state with eyes closed
• Resting-state with eyes open
• Visual Evoked Potentials at 2, 5, 7, 10 Hz - Standard checker-board pattern with pattern reversal
• Visual Evoked Potentials at 2, 5, 7, 10 Hz - Flashing with a plain colour, set as black

For more details regarding the experimental protocol and the design of the dataset, please refer to the associated publication: Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, 2021. (Under review)

Dataset structure and contents

The BED dataset contains EEG recordings from 21 subjects, acquired during 3 similar sessions for each subject. The sessions were spaced one week apart from each other.

The BED dataset includes:

• The raw EEG recordings with no pre-processing and the log files of the experimental procedure, in text format
• The EEG recordings with no pre-processing, segmented, structured and annotated according to the presented stimuli, in Matlab format
• The features extracted from each EEG segment, as described in the associated publication

The dataset is organised in 3 folders:

• RAW
• RAW_PARSED
• Features

RAW/ Contains the RAW files
RAW/sN/ Contains the RAW files associated with subject N
Each folder sN is composed by the following files:
- sN_s1.csv, sN_s2.csv, sN_s3.csv -- Files containing the EEG recordings for subject N and session 1, 2, and 3, respectively. These files contain 39 columns:
COUNTER INTERPOLATED F3 FC5 AF3 F7 T7 P7 O1 O2 P8 T8 F8 AF4 FC6 F4 ...UNUSED DATA... UNIX_TIMESTAMP
- subject_N_session_1_time_X.log, subject_N_session_2_time_X.log, subject_N_session_3_time_X.log -- Log files containing the sequence of events for the subject N and the session 1,2, and 3 respectively.

RAW_PARSED/
Contains Matlab files named sN_sM.mat. The files contain the recordings for the subject N in the session M. These files are composed by two variables:
- recording: size (time@256Hz x 17), Columns: COUNTER INTERPOLATED F3 FC5 AF3 F7 T7 P7 O1 O2 P8 T8 F8 AF4 FC6 F4 UNIX_TIMESTAMP
- events: cell array with size (events x 3) START_UNIX END_UNIX ADDITIONAL_INFO
START_UNIX is the UNIX timestamp in which the event starts
END_UNIX is the UNIX timestamp in which the event ends
ADDITIONAL INFO contains a struct with additional information regarding the specific event, in the case of the images, the expected score, the voted score, in the case of the cognitive task the input, in the case of the VEP the pattern and the frequency, etc..

Features/
Features/Identification
Features/Identification/[ARRC|MFCC|SPEC]/: Each of these folders contain the extracted features ready for classification for each of the stimuli, each file is composed by two variables, "feat" the feature matrix and "Y" the label matrix.
- feat: N x number of features
- Y: N x 2 (the #subject and the #session)
- INFO: Contains details about the event same as the ADDITIONAL INFO
Features/Verification: This folder is composed by 3 different files each of them with one different set of features extracted. Each file is composed by one cstruct array composed by:
- data: the time-series features, as described in the paper
- y: the #subject
- stimuli: the stimuli by name
- session: the #session
- INFO: Contains details about the event

The features provided are in sequential order, so index 1 and index 2, etc. are sequential in time if they belong to the same stimulus.

Additional information

For additional information regarding the creation of the BED dataset, please refer to the associated publication: Arnau-González, P., Katsigiannis, S., Arevalillo-Herráez, M., Ramzan, N., "BED: A new dataset for EEG-based biometrics", IEEE Internet of Things Journal, vol. 8, no. 15, pp. 12219 - 12230, 2021.

Gait recognition is the characterization of unqiue biometric patterns associated with each inidvidual which can be utilized to identify a person without direct contact. A public gain database with relatively large number of subjects can provide a great oppportunity to future studies to build and validate gait authentication models. The goal of this study is to introduce a comprehensive gait database of 93 human subjects who walked between two end points (320 meters) during two different sessions and record their gait data using two smart phones, one was attached to right thigh and another one on left side of waist. This data is collected with intention to be utilized by deep learning-based method which requires enough time points. The meta data including age, gender, smoking, daily exercise time, height, and weight of an individual is recorded. this data set is publicly available.

Except 19 subjects who did not attend for second session, every subject is associated with 4 different log files (each session contains two log files). Every file name has one of the following patterns: · sub0-lw-s1.csv: subject number 0, left waist, session 1 · sub0-rp-s1.csv: subject number 0, right thigh, session 1 · sub0-lw-s2.csv: subject number 0, left waist, session 2 · sub0-rp-s2.csv: subject number 0, right thigh, session 2 Every log file contains 58 features that are internally captured and calculated using SensorLog app. Additionally, an Excel file contain the meta data is provided for each subject.

Face Antispoofing dataset for recognition systems

The dataset consists of 98,000 videos and selfies from 170 countries, providing a foundation for developing robust security systems and facial recognition algorithms.

While the dataset itself doesn't contain spoofing attacks, it's a valuable resource for testing liveness detection system, allowing researchers to simulate attacks and evaluate how effectively their systems can distinguish between real faces and various forms of spoofing.

By utilizing this dataset, researchers can contribute to the development of advanced security solutions, enabling the safe and reliable use of biometric technologies for authentication and verification. - Get the data

Examples of data

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2Fe46e401a5449bacce5f934aaea9bb06e%2FFrame%20155.png?generation=1730591437955112&alt=media" alt=""> The dataset offers a high-quality collection of videos and photos, including selfies taken with a range of popular smartphones, like iPhone, Xiaomi, Samsung, and more. The videos showcase individuals turning their heads in various directions, providing a natural range of movements for liveness detection training.

💵 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.

Metadata for the dataset

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F22059654%2F8350718e93ee92840995405815739c61%2FFrame%20136%20(1).png?generation=1730591760432249&alt=media" alt=""> Furthermore, the dataset provides detailed metadata for each set, including information like gender, age, ethnicity, video resolution, duration, and frames per second. This rich metadata provides crucial context for analysis and model development.

Researchers can develop more accurate liveness detection algorithms, which is crucial for achieving the iBeta Level 2 certification, a benchmark for robust and reliable biometric systems that prevent fraud.

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

iBeta Level 1 - liveness detection dataset

The dataset was collected specifically for iBeta requirements for Level 1 certification of the Liveness Detection system.

💴 For Commercial Usage: Full version of the dataset includes 42,000 video attacks, leave a request on TrainingData to buy the dataset

The dataset consists of more than 42,000 video attacks of 7 different types specifically curated for evaluating liveness detection algorithms. The dataset aims to incorporate different scenarios and challenges to enable robust assessment and comparison of liveness detection systems.

The iBeta Liveness Detection Level 1 dataset serves as a benchmark for the development and assessment of liveness detection systems and for evaluating and improving the performance of algorithms.

Types of attacks:

1. Outline: printed outlines of people's photos.
2. Outline3d: printed portraits of people attached to a cylinder.
3. Mask: printed portraits of people with cut-out eyes in various variations.
4. Mask3d: portraits consisting of several connected cardboard masks.
5. Phone: demonstrations of a person's photo on the smartphone.
6. Monitor: demonstrations of a person's photo on a computer screen.
7. Real: a real human face

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F72c514066335ed6b6d1d273d1c4198ef%2FMASK_TYPES.png?generation=1678913343424357&alt=media" alt="">

Each attack was filmed on an Apple iPhone and Google Pixel. The videos were filmed on various backgrounds and using additional accessories such as faked facial hair, scarves, hats, and others.

The dataset comprises videos of genuine facial presentations using various methods, including 3D masks and photos, as well as real and spoof faces. It proposes a novel approach that learns and extracts facial features to prevent spoofing attacks, based on deep neural networks and advanced biometric techniques.

Our results show that this technology works effectively in securing most applications and prevents unauthorized access by distinguishing between genuine and spoofed inputs. Additionally, it addresses the challenging task of identifying unseen spoofing cues, making it one of the most effective techniques in the field of anti-spoofing research.

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to discuss your requirements, learn about the price and buy the dataset

iBeta Level 2 Liveness Detection

Try the iBeta Level 2 dataset: Link to the Kaggle

TrainingData provides high-quality data annotation tailored to your needs.

keywords: ibeta level 1, ibeta level 2, liveness detection systems, liveness detection dataset, biometric dataset, biometric data dataset, replay attack dataset, biometric system attacks, anti-spoofing dataset, face liveness detection, deep learning dataset, face spoofing database, face anti-spoofing, presentation attack detection, presentation attack dataset, 2D print attacks, 3D print attacks, phone attack dataset, face anti spoofing, large-scale face anti spoofing, rich annotations anti spoofing dataset, cut prints spoof attack

Off line signature database. It contains data from 960 individuals: 24 genuine signatures for each individual, plus 30 forgeries of his/her signature. The 24 genuine specimens of each signer were collected in a single day writing sessions. The forgeries were produced from the static image of the genuine signature. Each forger was allowed to practice the signature for as long as s/he wishes. Each forger imitated 3 signatures of 5 signers in a single day writing session. The genuine signatures shown to each forger are chosen randomly from the 24 genuine ones. Therefore for each genuine signature there are 30 skilled forgeries made by 10 forgers from 10 different genuine specimens. The signatures are in "bmp" format, in black and white and 300 dpi. The files of the genuine signatures are named xxx\c-xxx-yy.bmp and the files of the forgeries are named xxx\cf-xxx-yy.bmp where xxx is the number of the signer and yy its repetition.

Download the license agreement, fill it out, sign it, and fax it to +34 928 451 243 (Attn. Miguel A. Ferrer) Send an email to gpds@gi.ulpgc.es, as follows: Subject DATABASE download Body: Your name, e-mail, phone number, organization, postal mail, Database you require, purpose for which you will use the database, time and date at which you sent the fax with the signed license agreement. Along with the email send a pdf file with the signed license agreement. Send by postal mail the original of your signed license agreement to the following address: Miguel Ángel Ferrer Ballester Departamento de Señales y Comunicaciones Universidad de Las Palmas de Gran Canaria Campus de Tafira s/n 35017 Las Palmas de Gran Canaria, SPAIN Once the fax and email of the license agreement have been received, you will receive an email with instructions to download the database. After you finish the download, please notify by email that you have successfully completed the transaction. For more information, please contact: gpds@gi.ulpgc.es

The 3D Mask Attack Database (3DMAD) is a biometric (face) spoofing database. It contains 76500 frames of 17 persons, recorded using Kinect for both real-access and spoofing attacks. Each frame consists of:

a depth image (640x480 pixels – 1x11 bits)

the corresponding RGB image (640x480 pixels – 3x8 bits)

manually annotated eye positions (with respect to the RGB image).

The data is collected in 3 different sessions for all subjects and for each session 5 videos of 300 frames are captured. The recordings are done under controlled conditions, with frontal-view and neutral expression. The first two sessions are dedicated to the real access samples, in which subjects are recorded with a time delay of ~2 weeks between the acquisitions. In the third session, 3D mask attacks are captured by a single operator (attacker).

In each video, the eye-positions are manually labelled for every 1st, 61st, 121st, 181st, 241st and 300th frames and they are linearly interpolated for the rest.

The real-size masks are obtained using "ThatsMyFace.com". The database additionally contains the face images used to generate these masks (1 frontal and 2 profiles) and paper-cut masks that are also produced by the same service and using the same images.

The satellite package which contains the Bob accessor methods to use this database directly from Python, with the certified protocols, is available in two different distribution formats:

You can download it from PyPI, or

You can download it in its source form from its git repository.

Acknowledgments

If you use this database, please cite the following publication:

Nesli Erdogmus and Sébastien Marcel, "Spoofing in 2D Face Recognition with 3D Masks and Anti-spoofing with Kinect", Biometrics: Theory, Applications and Systems, 2013. 10.1109/BTAS.2013.6712688 https://publications.idiap.ch/index.php/publications/show/2657

Hair Detection & Segmentation Dataset

The dataset consists of images of people for detection and segmentation of hairs within the oval region of the face. It primarily focuses on identifying the presence of hair strands within the facial area and accurately segmenting them for further analysis or applications.

The dataset contains a diverse collection of images depicting people with different hair styles, colors, lengths, and textures. Each image is annotated with annotations that indicate the boundaries and contours of the individual hair strands within the oval of the face.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on TrainingData to buy the dataset

The dataset can be utilized for various purposes, such as developing machine learning models or algorithms for hair detection and segmentation. It can also be used for research in facial recognition, virtual try-on applications, hairstyle recommendation systems, and other related areas.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F81b5a9e6c755e04d97fc6b175a127432%2FMacBook%20Air%20-%201.png?generation=1691561622573906&alt=media" alt="">

SIMILAR DATASETS:

• Bald People Segmentation Dataset
• Dataset of bald people, 5000 images
• Body Segmentation - 5,300 Photos
• Selfies, ID Images dataset (5591 sets of 15 files)
• Face segmentation

Dataset structure

• images - contains of original images of people
• masks - includes segmentation masks for the original images
• collages - includes original images with colored hairs within the oval of the face
• annotations.xml - contains coordinates of the bounding boxes and labels, 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 bounding boxes and labels for parking spaces. For each point, the x and y coordinates are provided.

Tags for the images:

• is_hair - hair area
• no_hair - area of no hair

Example of XML file structure

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2Fb634cd569d4bf7a253ac7a0e7a91ef7e%2Fcarbon.png?generation=1691562068420789&alt=media" alt="">

Hair Detection & Segmentation might be made in accordance with your requirements.

💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to discuss your requirements, learn about the price and buy the dataset

TrainingData provides high-quality data annotation tailored to your needs

keywords: biometric dataset, biometric data dataset, face recognition database, face recognition dataset, face detection dataset, facial analysis, human images dataset, hair detection, hair segmentation,human hair segmentation, image segmentation, images dataset, computer vision, deep learning dataset, scalp, augmented reality, ar