---
license: cc-by-nc-nd-4.0
task_categories:
- image-segmentation
language:
- en
tags:
- ibeta
- replay attack
- video
- liveness detection
- biometric
- anti-spoofing
dataset_info:
  features:
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: id
    dtype: string
  - name: gender
    dtype: string
  - name: age
    dtype: int8
  splits:
  - name: train
    num_bytes: 44991960
    num_examples: 20
  download_size: 44094250
  dataset_size: 44991960
size_categories:
- 10K<n<100K
---
# Face segmentation
An example of a dataset that we've collected for a photo edit App. The dataset includes 20 selfies of people (man and women) in segmentation masks and their visualisations.

# The dataset is created on the basis of [iBeta Level 1 Dataset](https://unidata.pro/datasets/ibeta-level-1-video-attacks/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=face_segmentation)

# File with the extension .csv
includes the following information for each media file:

- **Image**: the link to access the media file
- **Mask**: the link to access the segmentation mask for the Image

# The folder "images"
Contains the original selfies of people.

# The folder "masks"
Includes segmentation masks for the photos:
- corresponding to the images in the previous folder
- identified by the same file names.

**How it works**: *go to the "masks" folder and make sure that the file "1.png" is a segmentation mask of the selfi, created for the photo "1.png" in the "images" folder.*

##  👉 Legally sourced datasets and carefully structured for AI training and model development. Explore samples from our dataset of 25,000+ human images & videos - [Full dataset](https://unidata.pro/datasets/ibeta-level-1-video-attacks/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=face_segmentation)

#**🚀 You can learn more about our high-quality unique datasets [here](https://unidata.pro/datasets/ibeta-level-1-video-attacks/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=face_segmentation)**


*keywords: head segmentation dataset, face-generation, semantic segmentation, face parts recognition, human faces, portrait segmentation, human face extraction, image segmentation, annotation, biometric dataset, biometric data dataset, face recognition database, facial recognition, face forgery detection, face shape, facial gestures, ar, augmented reality, face recognition dataset, face detection dataset, facial analysis, human images dataset*