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--- |
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language: |
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- en |
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license: cc-by-nc-nd-4.0 |
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task_categories: |
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- video-classification |
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tags: |
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- code |
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- legal |
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- finance |
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dataset_info: |
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features: |
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- name: photo |
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dtype: image |
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- name: video |
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dtype: string |
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- name: worker_id |
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dtype: string |
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- name: set_id |
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dtype: string |
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- name: age |
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dtype: int8 |
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- name: country |
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dtype: string |
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- name: gender |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 45568435 |
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num_examples: 14 |
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download_size: 458883249 |
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dataset_size: 45568435 |
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--- |
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# Presentation Attack Detection 2D Dataset - Biometric Attack Dataset |
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The liveness detection dataset consists of photos of individuals and videos of him/her wearing printed 2D mask with cut-out holes for eyes. Videos are filmed in different lightning conditions and in different places (*indoors, outdoors*), a person moves his/her head left, right, up and down. Each video in the dataset has an approximate duration of 15-17 seconds. |
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# 💴 For Commercial Usage: Full version of the dataset includes much more videos of people for presentation attack detection, leave a request on **[TrainingData](https://trainingdata.pro/datasets?utm_source=huggingface&utm_medium=cpc&utm_campaign=presentation-attack-detection-2d-dataset)** to buy the dataset |
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### Types of media files in the dataset: |
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- **Photo** of the individual |
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- **Video** with the printed photo of the individual, mask is cut along the contour, there are cut-out holes for eyes, mask is attached to the person's head |
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The dataset comprises videos of genuine facial presentations using various methods, including 2D 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. |
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The dataset serves as a valuable resource for computer vision, anti-spoofing tasks, video analysis, and security systems. It allows for the development of algorithms and models that can effectively detect attacks perpetrated by individuals wearing printed 2D masks. |
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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. |
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# 💴 Buy the Dataset: This is just an example of the data. Leave a request on **[https://trainingdata.pro/datasets](https://trainingdata.pro/datasets?utm_source=huggingface&utm_medium=cpc&utm_campaign=presentation-attack-detection-2d-dataset) to discuss your requirements, learn about the price and buy the dataset** |
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# Content |
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### The folder **"files"** includes 14 folders: |
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- corresponding to each person in the sample |
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- including photo and video of the individual |
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### File with the extension .csv |
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includes the following information for each media file: |
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- **set_id**: the identifier of the set of media files, |
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- **worker_id**: the identifier of the person who provided the media file, |
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- **age**: the age of the person, |
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- **gender**: the gender of the person, |
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- **country**: the country of origin of the person |
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## **[TrainingData](https://trainingdata.pro/datasets?utm_source=huggingface&utm_medium=cpc&utm_campaign=presentation-attack-detection-2d-dataset)** provides high-quality data annotation tailored to your needs |
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More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets** |
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TrainingData's GitHub: **https://github.com/Trainingdata-datamarket/TrainingData_All_datasets** |
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*keywords: ibeta level 1, ibeta level 2, liveness detection systems, liveness detection dataset, biometric dataset, biometric data dataset, biometric system attacks, anti-spoofing dataset, face liveness detection, deep learning dataset, face spoofing database, face anti-spoofing, face recognition, face detection, face identification, human video dataset, video dataset, presentation attack detection, presentation attack dataset, 2d print attacks, print 2d attacks dataset, phone attack dataset, face anti spoofing, large-scale face anti spoofing, rich annotations anti spoofing dataset, cut prints spoof attack* |
