Face Anti Spoofing Dataset - 98 000+ files

Dataset features 98,000+ files of real photos and videos of people from 170+ countries, representing 70,000+ unique individuals. By leveraging this dataset, developers can enhance spoofing detection techniques, improve recognition systems, and deploy anti-spoofing algorithms capable of preventing fraud in deep learning-based solutions.- Get the data

  Dataset characteristics:

Characteristic Data

  Description

Live… See the full description on the dataset page: https://huggingface.co/datasets/ud-biometrics/Anti-Spoofing-Real-Videos.

Liveness Detection - Video Classification

The dataset is created on the basis of Anti Spoofing Real Dataset and Antispoofing Replay Dataset

The biometric attack dataset is created for video classification of real videos and replay attacks on these videos. Replay attack involves presenting a pre-recorded video or previously captured footage as if it were occurring in real-time. The primary objective is to distinguish between genuine, real-time footage and manipulated recordings.

The videos were gathered by capturing faces of genuine individuals presenting spoofs, using facial presentations. Our dataset proposes a novel approach that learns and detects spoofing techniques, extracting features from the genuine facial images to prevent the capturing of such information by fake users.

The dataset contains images and videos of real humans with various resolutions, views, and colors, making it a comprehensive resource for researchers working on anti-spoofing technologies.

Types of videos in the dataset:

• real - real video of the person
• attack - real video played on a phone and filmed on the other phone (replay attack)

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F12421376%2F98ee9f9f7de3bc0abfee003525eee0cc%2FFrame%2040.png?generation=1698694139067531&alt=media" alt="">

The dataset provides data to combine and apply different techniques, approaches, and models to address the challenging task of distinguishing between genuine and spoofed inputs, providing effective anti-spoofing solutions in active authentication systems. These solutions are crucial as newer devices, such as phones, have become vulnerable to spoofing attacks due to the availability of technologies that can create replays, reflections, and depths, making them susceptible to spoofing and generalization.

Our dataset also explores the use of neural architectures, such as deep neural networks, to facilitate the identification of distinguishing patterns and textures in different regions of the face, increasing the accuracy and generalizability of the anti-spoofing models.

💴 For Commercial Usage: Full version of the dataset includes 30 000+ videos, leave a request on TrainingData to buy the dataset

Metadata for the full dataset:

• replay.assignment_id - unique identifier of the media file
• real_assignment_id- unique identifier of the media file from the Antispoofing Real Dataset
• 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
• video_extension - video extensions in the dataset
• video_resolution - video resolution in the dataset
• video_duration - video duration in the dataset
• video_fps - frames per second for video in the dataset

OTHER BIOMETRIC DATASETS:

• 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: - real_video - real videos of the people, - attack - real videos played on a phone and filmed on the other phone (replay attack)

File with the extension .csv

• file: link to access the file,
• type: type of the video (real or attack),
• split: classification on train and test

TrainingData provides high-quality data annotation tailored to your needs

*keywords: liveness detection systems, liveness detection dataset, biometric dataset, biometric data dataset, biometric system attacks, anti-spoofing datas...

Real photos and videos of people for anti-spoofing. The dataset can be used to distinguish real users from scammers

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

Live Face Anti-Spoof Dataset

A live face dataset is crucial for advancing computer vision tasks such as face detection, anti-spoofing detection, and face recognition. The Live Face Anti-Spoof Dataset offered by Ainnotate is specifically designed to train algorithms for anti-spoofing purposes, ensuring that AI systems can accurately differentiate between real and fake faces in various scenarios.

Key Features:

Comprehensive Video Collection: The dataset features thousands of videos showcasing a diverse range of individuals, including males and females, with and without glasses. It also includes men with beards, mustaches, and clean-shaven faces. Lighting Conditions: Videos are captured in both indoor and outdoor environments, ensuring that the data covers a wide range of lighting conditions, making it highly applicable for real-world use. Data Collection Method: Our datasets are gathered through a community-driven approach, leveraging our extensive network of over 700k users across various Telegram apps. This method ensures that the data is not only diverse but also ethically sourced with full consent from participants, providing reliable and real-world applicable data for training AI models. Versatility: This dataset is ideal for training models in face detection, anti-spoofing, and face recognition tasks, offering robust support for these essential computer vision applications. In addition to the Live Face Anti-Spoof Dataset, FileMarket provides specialized datasets across various categories to support a wide range of AI and machine learning projects:

Object Detection Data: Perfect for training AI in image and video analysis. Machine Learning (ML) Data: Offers a broad spectrum of applications, from predictive analytics to natural language processing (NLP). Large Language Model (LLM) Data: Designed to support text generation, chatbots, and machine translation models. Deep Learning (DL) Data: Essential for developing complex neural networks and deep learning models. Biometric Data: Includes diverse datasets for facial recognition, fingerprint analysis, and other biometric applications. This live face dataset, alongside our other specialized data categories, empowers your AI projects by providing high-quality, diverse, and comprehensive datasets. Whether your focus is on anti-spoofing detection, face recognition, or other biometric and machine learning tasks, our data offerings are tailored to meet your specific needs.

Liveness Detection Video Dataset with videos and images of people for training anti-spoofing and biometric authentication

Replay-Attack is a dataset for face recognition and presentation attack detection (anti-spoofing). The dataset consists of 1300 video clips of photo and video presentation attack (spoofing attacks) to 50 clients, under different lighting conditions.

Spoofing Attacks Description

The 2D face spoofing attack database consists of 1,300 video clips of photo and video attack attempts of 50 clients, under different lighting conditions.

The data is split into 4 sub-groups comprising:

Training data ("train"), to be used for training your anti-spoof classifier;

Development data ("devel"), to be used for threshold estimation;

Test data ("test"), with which to report error figures;

Enrollment data ("enroll"), that can be used to verify spoofing sensitivity on face detection algorithms.

Clients that appear in one of the data sets (train, devel or test) do not appear in any other set.

Database Description

All videos are generated by either having a (real) client trying to access a laptop through a built-in webcam or by displaying a photo or a video recording of the same client for at least 9 seconds. The webcam produces colour videos with a resolution of 320 pixels (width) by 240 pixels (height). The movies were recorded on a Macbook laptop using the QuickTime framework (codec: Motion JPEG) and saved into ".mov" files. The frame rate is about 25 Hz. Besides the native support on Apple computers, these files are easily readable using mplayer, ffmpeg or any other video utilities available under Linux or MS Windows systems.

Real client accesses as well as data collected for the attacks are taken under two different lighting conditions:

• controlled: The office light was turned on, blinds are down, background is homogeneous;
• adverse: Blinds up, more complex background, office lights are out.

To produce the attacks, high-resolution photos and videos from each client were taken under the same conditions as in their authentication sessions, using a Canon PowerShot SX150 IS camera, which records both 12.1 Mpixel photographs and 720p high-definition video clips. The way to perform the attacks can be divided into two subsets: the first subset is composed of videos generated using a stand to hold the client biometry ("fixed"). For the second set, the attacker holds the device used for the attack with their own hands. In total, 20 attack videos were registered for each client, 10 for each of the attacking modes just described:

4 x mobile attacks using an iPhone 3GS screen (with resolution 480x320 pixels) displaying:

1 x mobile photo/controlled

1 x mobile photo/adverse

1 x mobile video/controlled

1 x mobile video/adverse

4 x high-resolution screen attacks using an iPad (first generation, with a screen resolution of 1024x768 pixels) displaying:

1 x high-resolution photo/controlled

1 x high-resolution photo/adverse

1 x high-resolution video/controlled

1 x high-resolution video/adverse

2 x hard-copy print attacks (produced on a Triumph-Adler DCC 2520 color laser printer) occupying the whole available printing surface on A4 paper for the following samples:

1 x high-resolution print of photo/controlled

1 x high-resolution print of photo/adverse

The 1300 real-accesses and attacks videos were then divided in the following way:

Training set: contains 60 real-accesses and 300 attacks under different lighting conditions;

Development set: contains 60 real-accesses and 300 attacks under different lighting conditions;

Test set: contains 80 real-accesses and 400 attacks under different lighting conditions;

Face Locations

We also provide face locations automatically annotated by a cascade of classifiers based on a variant of Local Binary Patterns (LBP) referred as Modified Census Transform (MCT) [Face Detection with the Modified Census Transform, Froba, B. and Ernst, A., 2004, IEEE International Conference on Automatic Face and Gesture Recognition, pp. 91-96]. The automatic face localisation procedure works in more than 99% of the total number of frames acquired. This means that less than 1% of the total set of frames for all videos do not possess annotated faces. User algorithms must account for this fact.

Protocol for Licit Biometric Transactions

It is possible to measure the performance of baseline face recognition systems on the 2D Face spoofing database and evaluate how well the attacks pass such systems or how, otherwise robust they are to attacks. Here we describe how to use the available data at the enrolment set to create a background model, client models and how to perform scoring using the available data.

Universal Background Model (UBM): To generate the UBM, subselect the training-set client videos from the enrollment videos. There should be 2 per client, which means you get 30 videos, each with 375 frames to create the model;

Client models: To generate client models, use the enrollment data for clients at the development and test groups. There should be 2 videos per client (one for each light condition) once more. At the end of the enrollment procedure, the development set must have 1 model for each of the 15 clients available in that set. Similarly, for the test set, 1 model for each of the 20 clients available;

For a simple baseline verification, generate scores exhaustively for all videos from the development and test real-accesses respectively, but without intermixing accross development and test sets. The scores generated against matched client videos and models (within the subset, i.e. development or test) should be considered true client accesses, while all others impostors;

If you are looking for a single number to report on the performance do the following: exclusively using the scores from the development set, tune your baseline face recognition system on the EER of the development set and use this threshold to find the HTER on the test set scores.

Protocols for Spoofing Attacks

Attack protocols are used to evaluate the (binary classification) performance of counter-measures to spoof attacks. The database can be split into 6 different protocols according to the type of device used to generate the attack: print, mobile (phone), high-definition (tablet), photo, video or grand test (all types). Furthermore, subsetting can be achieved on the top of the previous 6 groups by classifying attacks as performed by the attacker bare hands or using a fixed support. This classification scheme makes-up a total of 18 protocols that can be used for studying the performance of counter-measures to 2D face spoofing attacks. The table bellow details the amount of video clips in each protocol.

Acknowledgements

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

I. Chingovska, A. Anjos, S. Marcel,"On the Effectiveness of Local Binary Patterns in Face Anti-spoofing"; IEEE BIOSIG, 2012. https://ieeexplore.ieee.org/document/6313548 http://publications.idiap.ch/index.php/publications/show/2447

Biometric Attack Dataset, Black People

  The similar dataset that includes all ethnicities - Anti Spoofing Real Dataset

The dataset for face anti spoofing and face recognition includes images and videos of black people. The dataset helps in enchancing the performance of the model by providing wider range of data for a specific ethnic group. The videos were gathered by capturing faces of genuine individuals presenting spoofs, using facial presentations. Our dataset proposes… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/black-people-liveness-detection-video-dataset.

Anti Spoofing Real - Liveness Detection dataset

The Biometric Attack dataset consists of 98,000 videos and selfies from people from 170 countries. The videos were gathered by capturing faces of genuine individuals presenting spoofs, using facial presentations. Our dataset proposes a novel approach that learns and detects spoofing techniques, extracting features from the genuine facial images to prevent the capturing of such information by fake users. The dataset contains images and… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/anti-spoofing_Real.

1. Specifications Data size : 200,000 ID

Population distribution : race distribution: Asians, Caucasians, black people; gender distribution: gender balance; age distribution: from child to the elderly, the young people and the middle aged are the majorities

Collection environment : indoor scenes, outdoor scenes

Collection diversity : various postures, expressions, light condition, scenes, time periods and distances

Collection device : iPhone, android phone, iPad

Collection time : daytime,night

Image Parameter : the video format is .mov or .mp4, the image format is .jpg

Accuracy : the accuracy of actions exceeds 97%

1. About Nexdata Nexdata owns off-the-shelf PB-level Large Language Model(LLM) Data, 1 million hours of Audio Data and 800TB of Annotated Imagery Data. These ready-to-go machine learning (ML) data supports instant delivery, quickly improve the accuracy of AI models. For more details, please visit us at https://www.nexdata.ai/datasets/computervision?source=Datarade

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

The dataset consists of waist-high selfies and video of real people. The dataset solves tasks in the field of anti-spoofing and it is useful for buisness and safety systems.

Off-the-shelf face anti-spoofing data covers 2D/3D liveness detection, infrared face, gait recognition and re-id. All the anti-spoofing data is collected with consent and has obtained Level 2 iBeta certificate.

Anti-Spoofing dataset: live, replay, cut, print, 3D masks - large-scale face anti spoofing

This dataset delivers a single, end-to-end resource for training and benchmarking facial liveness-detection systems. By aggregating live sessions and eleven realistic presentation-attack classes into one collection, it accelerates development toward iBeta Level 1/2 compliance and strengthens model robustness against the full spectrum of spoofing tactics

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F20109613%2F6432e95d7b7fef1d271457f172e11e0c%2FFrame%20103-3.png?generation=1753867895186569&alt=media" alt="">

Why Comprehensive Anti-Spoofing Data?

Modern certification pipelines demand proof that a system resists all common attack vectors—not just prints or replays. This dataset delivers those vectors in one place, allowing you to: - Benchmark a model’s true generalisation - Fine-tune against rare but high-impact threats (e.g., silicone or textile masks) - Streamline audits by demonstrating coverage of every ISO 30107-3 attack category

Dataset Features

• Dataset Size: ≈ 95 000 videos / image sequences spanning live captures and eleven spoof classes
• Attack Diversity: 3D paper mask, wrapped 3D mask, photo print, mobile replay, display replay, cut-out 2D mask, silicone mask, latex mask, textile mask
• Active Liveness Cues: Natural blinks, and head rotations included across live and mask sessions
• Attribute Range: different combinations of hairstyles, eyewear, facial hair, and accessories.
• Environmental Variability: Indoor/outdoor scenes under various lighting conditions
• Multi-angle Capture: Mainly used selfie camera, also back
• Capture Devices: Footage from flagship and mid-range phones (iPhone 14 / 13 Pro, Galaxy S23, Pixel 7, Redmi Note 12 Pro+, Galaxy A54, Honor 70)
• Additional Flexibility: Custom re-captures available on request

Full version of dataset is availible for commercial usage - leave a request on our website Axonlabs to purchase the dataset 💰

Technical Specifications

• File Format: MP4 for video, JPEG/PNG for still sequences; all compatible with mainstream ML frameworks
• Resolution & FPS: Up to 4K @ 60 fps; balanced presets included for rapid training

Best Uses

Ideal for companies pursuing or maintaining iBeta Level 1/2 certification, research groups exploring new PAD architectures, and vendors stress-testing production face-verification pipelines

Attack Classes

• Live / Genuine Natural faces with spontaneous movements across varied devices and lighting
• 3D Paper Mask Folded paper masks with protruding nose/forehead
• Wrapped 3D Print Rigid paper moulds reproducing head geometry
• Photo Print Glossy still photos at multiple angles—the classic 2D spoof
• Cylinder 3D Paper Mask A folded or cylindrical sheet of paper that simulates volume
• Mobile Replay Face videos played on phone screens; includes glare and auto-brightness shifts
• Display Replay Attacks via monitors, and laptops
• Cut-out 2D Mask Flat printed masks with eye/mouth holes plus active head motion
• On-actor Print / Cuts Paper elements (photos, cutouts) are glued directly onto the actor's face
• Silicone and Latex Masks High-detail silicone/latex overlays with blinking and subtle mimicry
• Cloth 3D Mask Elastic fabric masks hugging facial contours during movement
• High-Fidelity Resin Mask Hyperrealistic masks with detailed skin texture

Conclusion

This dataset’s scale, breadth of attack types, and real-world capture conditions make it indispensable for anyone building or evaluating biometric anti-spoofing solutions. Deploy it to harden your systems against today’s—and tomorrow’s—most sophisticated presentation attacks

Biometric Attack Dataset, Hispanic People

  The similar dataset that includes all ethnicities - Anti Spoofing Real Dataset

The dataset for face anti spoofing and face recognition includes images and videos of hispanic people. 32,600+ photos & video of 16,300 people from 20 countries. The dataset helps in enchancing the performance of the model by providing wider range of data for a specific ethnic group. The videos were gathered by capturing faces of genuine individuals… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/hispanic-people-liveness-detection-video-dataset.

Replay-Mobile is a dataset for face recognition and presentation attack detection (anti-spoofing). The dataset consists of 1190 video clips of photo and video presentation attacks (spoofing attacks) to 40 clients, under different lighting conditions. These videos were recorded with an iPad Mini2 (running iOS) and a LG-G4 smartphone (running Android).

Database Description

All videos have been captured using the front-camera of the mobile device (tablet or phone). The front-camera produces colour videos with a resolution of 720 pixels (width) by 1280 pixels (height) and saved in ".mov" file-format. The frame rate is about 25 Hz. Real-accesses have been performed by the genuine user (presenting one's true face to the device). Attack-accesses have been performed by displaying a photo or a video recording of the attacked client, for at least 10 seconds.

Real client accesses have been recorded under five different lighting conditions (controlled, adverse, direct, lateral and diffuse). In addition, to produce the attacks, high-resolution photos and videos from each client were taken under conditions similar to those in their authentication sessions (lighton, lightoff).

The 1190 real-accesses and attacks videos were then grouped in the following way:

Training set: contains 120 real-accesses and 192 attacks under different lighting conditions;

Development set: contains 160 real-accesses and 256 attacks under different lighting conditions;

Test set: contains 110 real-accesses and 192 attacks under different lighting conditions;

Enrollment set: contains 160 real-accesses under different lighting conditions, to be used exclusively for studying the baseline performance of face recognition systems. (This set is again partitioned into 'Training', 'Development' and 'Test' sets.)

Attacks

For photos attacks a Nikon coolix P520 camera, which records 18Mpixel photographs, has been used. Video attacks were captured using the back-camera of a smartphone LG-G4, which records 1080p FHD video clips using its 16 Mpixel camera.

Attacks have been performed in two ways:

A matte-screen was used to perform the attacks (i.e., to display the digital photo or video of the attacked identity). For all such (matte-screen) attacks, a stand was used to hold capturing devices.

Print attacks. For "fixed" attacks, both capturing devices were supported on a stand (as for matte-screen attacks). For "hand" attacks, the spoofer held the capturing device in his/her own hands while the spoof-resource (printed photo) was stationary.

In total, 16 attack videos were registered for each client, 8 for each of the attacking modes described above.

4 x mobile attacks using an Philips 227ELH screen (with resolution 1920x1080 pixels)

4 x tablet attacks using an Philips 227ELH screen (with resolution 1920x1080 pixels)

2 x mobile attacks using hard-copy print attacks fixed (produced on a Konica Minolta ineo+ 224e color laser printer) occupying the whole available printing surface on A4 paper

2 x mobile attacks using hard-copy print attacks fixed (produced on a Konica Minolta ineo+ 224e color laser printer) occupying the whole available printing surface on A4 paper

2 x mobile attacks using hard-copy print attacks hand (produced on a Konica Minolta ineo+ 224e color laser printer) occupying the whole available printing surface on A4 paper

2 x mobile attacks using hard-copy print attacks hand (produced on a Konica Minolta ineo+ 224e color laser printer) occupying the whole available printing surface on A4 paper

Reference

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

Artur Costa-Pazo, Sushil Bhattacharjee, Esteban Vazquez-Fernandez and Sébastien Marcel,"The REPLAY-MOBILE Face Presentation-Attack Database", IEEE BIOSIG 2016. 10.1109/BIOSIG.2016.7736936 http://publications.idiap.ch/index.php/publications/show/3477

Biometric Attack Dataset, Asian People

  The similar dataset that includes all ethnicities - Anti Spoofing Real Dataset

The dataset for face anti spoofing and face recognition includes images and videos of asian people. 30,600+ photos & video of 15,300 people from 32 countries. All people presented in the dataset are South Asian, East Asian or Middle Asian. The dataset helps in enchancing the performance of the model by providing wider range of data for a specific ethnic… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/asian-people-liveness-detection-video-dataset.

Dataset with phone and webcam videos for anti-spoofing, biometric verification, facial recognition, and access control security

Liveness Detection Dataset: iBeta level 2 advanced mask attacks (5 K videos)

Anti-Spoofing Paper Attacks iBeta 2 - 5,000 videos 4 different attack types, advanced paper attacks for Liveness Detection

  Full version of dataset is availible for commercial usage - leave a request on our website Axonlabs to purchase the dataset 💰







  Types of Presentation Attacks (paper masks)

1. Printed attributes on photo – a flat facial photo with accessories (e.g., glasses, hat)… See the full description on the dataset page: https://huggingface.co/datasets/AxonData/face-anti-spoofing-advanced-paper-attacks.

Biometric Attack Dataset, Caucasian People

  The similar dataset that includes all ethnicities - Anti Spoofing Real Dataset

The dataset for face anti spoofing and face recognition includes images and videos of сaucasian people. The dataset helps in enchancing the performance of the model by providing wider range of data for a specific ethnic group. The videos were gathered by capturing faces of genuine individuals presenting spoofs, using facial presentations. Our dataset… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/caucasian-people-liveness-detection-dataset.