Dataset

This dataset was created by Divya Nayan

Contents

Face Segmentation

## Overview

Face Segmentation is a dataset for instance segmentation tasks - it contains Face annotations for 464 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).

Dataset

This dataset was created by Nikdintel

Released under Apache 2.0

Contents

This Human Face Segmentation Dataset contains 70,846 high-quality images featuring diverse subjects with pixel-level annotations. The dataset includes individuals across various age groups—from young children to the elderly—and represents multiple ethnicities, including Asian, Black, and Caucasian. Both males and females are included. The scenes range from indoor to outdoor environments, with pure-color backgrounds also present. Facial expressions vary from neutral to complex, including large-angle head tilts, eye closures, glowers, puckers, open mouths, and more. Each image is precisely annotated on a pixel-by-pixel basis, covering facial regions, five sense organs, body parts, and appendages. This dataset is ideal for applications such as facial recognition, segmentation, and other computer vision tasks involving human face parsing.

Face Segmentation Dataset This dataset provides resources for face segmentation, featuring segmentation masks generated from bounding box-based object detection data. Dataset Structure The dataset is organized as follows: / ├── images/ # Original image files │ ├── *.jpg │ └── *.png # Segmentation masks (binary PNG files)

File Formats Image Files (images/)

Contains original face images Supported formats: JPG, JPEG, PNG

Segmentation Masks (segment/)

Binary segmentation masks (PNG) corresponding to each image All masks have the same resolution as their original images Pixel values:

0: Background 255: Face region

Dataset Characteristics

All masks are filtered to have a white region ratio between 1% and 20% Small clusters and noise have been removed using morphological operations For images with multiple faces, all face regions are integrated into a single mask

Rosa Face Mask Segmentation

## Overview

Rosa Face Mask Segmentation is a dataset for instance segmentation tasks - it contains Facemask annotations for 975 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).

Image Dataset of Face Segmentation for recognition tasks

Dataset comprises 87,800+ images annotated with 100+ landmarks, providing a comprehensive foundation for research in face recognition, segmentation tasks, and object recognition. It is designed to support the development of learning models, recognition algorithms, and segmentation techniques. By utilizing this dataset, researchers and developers can advance their understanding and capabilities in facial recognition, face… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/face-segmentation-image-dataset.

This Human Face Segmentation Dataset contains 70,846 high-quality images featuring diverse subjects with pixel-level annotations. The dataset includes individuals across various age groups—from young children to the elderly—and represents multiple ethnicities, including Asian, Black, and Caucasian. Both males and females are included. The scenes range from indoor to outdoor environments, with pure-color backgrounds also present. Facial expressions vary from neutral to complex, including large-angle head tilts, eye closures, glowers, puckers, open mouths, and more. Each image is precisely annotated on a pixel-by-pixel basis, covering facial regions, five sense organs, body parts, and appendages. This dataset is ideal for applications such as facial recognition, segmentation, and other computer vision tasks involving human face parsing.

Data size 70,846 images, there is only one face in an image

Population distribution race distribution: 32,235 images of Asian, 29,501 images of Caucasian, 9,110 images of black race; gender distribution: 34,044 male images and 36,802 female images; age distribution: baby, teenager, young, midlife and senior

Collection environment including pure color background, indoor scenes and outdoor scenes

Data diversity multiple scenes, multiple ages, multiple races, complicated expressions (closing eye, glower, pucker, opening mouth, etc.), and multiple appendages

Image Parameter Data format: the image data is in .jpg or .png format, the annotation file is in .json or .psd format; the human face resolution is not lower than 128128, and pupillary distance is not less than 60 pixels

Annotation content segmentation annotation of human face, the five sense organs, body and appendages

Accuracy the mask edge location errors in x and y directions are less than 3 pixels, which is considered as a qualified annotation; the annotation part (id) is

Dog Face Segmentation

## Overview

Dog Face Segmentation is a dataset for instance segmentation tasks - it contains Objects annotations for 507 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).

Humanscape Face Segmentation

## Overview

Humanscape Face Segmentation is a dataset for instance segmentation tasks - it contains Face annotations for 671 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).

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.

This project provides a custom face segmentation dataset designed for deep learning-based recognition systems. It was used in our publication on real-time face recognition with YOLOv8. If you use this dataset, please cite the following article: H. S. Mahdi et al., "Accelerated real‑time face recognition and segmentation with YOLOv8 optimized through Tensor RT," JISEM, vol. 10, no. 35s, Apr. 2025.

This Human Face Segmentation Dataset contains 70,846 high-quality images featuring diverse subjects with pixel-level annotations.

The Asian Face Occlusion Dataset is tailored for the visual entertainment industry, comprising a vast collection of internet-collected images, each with a resolution exceeding 2736 x 3648 pixels. This dataset focuses on instance and semantic segmentation of Asian faces, specifically targeting individuals aged between 18 and 50 with a male-to-female ratio of 3:7. The unique aspect of this dataset is the inclusion of various face-covering items, providing a diverse range of occlusion scenarios.

EasyPortrait - Face Parsing and Portrait Segmentation Dataset

We introduce a large-scale image dataset EasyPortrait for portrait segmentation and face parsing. Proposed dataset can be used in several tasks, such as background removal in conference applications, teeth whitening, face skin enhancement, red eye removal or eye colorization, and so on. EasyPortrait dataset size is about 26GB, and it contains 20 000 RGB images (~17.5K FullHD images) with high quality annotated masks.… See the full description on the dataset page: https://huggingface.co/datasets/gofixyourself/EasyPortrait.

Matting (hair&faces) - faces dataset

Accurately estimated foreground object in images. Dataset for editing applications for creating visual effects.

💴 For Commercial Usage: To discuss your requirements, learn about the price and buy the dataset, leave a request on roman@kucev.com to buy the dataset

Content

Includes 2 folders: - images - original images of faces - masks - matting masks for images

💴 Buy the Dataset: This is just an example of the data. Leave a request on roman@kucev.com to discuss your requirements, learn about the price and buy the dataset.

keywords: head segmentation dataset, face-generation, segmentation, 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, ar, augmented reality, face detection dataset, facial analysis, human images dataset, hair segmentation, matting, image matting, computer vision, deep learning, potrait matting, natural image matting

The Head and Neck Semantic Segmentation Dataset is designed for the e-commerce & retail and media & entertainment sectors, featuring a collection of AI-generated cartoon images with resolutions above 1024 x 1024 pixels. This dataset focuses on semantic segmentation, specifically targeting the main character's head, including face, hair, and any accessories, as well as the neck area up to the collarbone, with an allowance for small, unsegmented parts on the edges.

21,299 Images of Human Body and Face Segmentation Data. The data includes indoor scenes and outdoor scenes. The data covers female people and male people. The race distribution includes Asian, black race and Caucasian. The age distribution ranges from teenager to the elderly, the middle-aged and young people are the majorities. The dataset diversity includes multiple scenes, ages, races, postures, and appendages. In terms of annotation, we adpoted pixel-wise segmentation annotations on human face, the five sense organs, body and appendages. The data can be used for tasks such as human body segmentation.

Description Human Face Segmentation Data from 70,846 Images. Pure color backgrounds, interior and exterior scene types are all included in the data. Both males and females are included in the data. Asian, Black, and Caucasian races are represented in the race distribution. The age ranges from young children to elderly people. Simple and complex facial expressions can be found in the data (large-angle tilt of face, closing eye, glower, pucker, opening mouth, etc.). We used pixel-by-pixel segmentation annotations to annotate the human face, the five sense organs, the body, and appendages. The information can be applied to tasks like facial Recon Related Tasks For more details, please visit: https://www.nexdata.ai/datasets/computervision/945?source=Kaggle

Specifications Data size 70,846 images, there is only one face in an image Population distribution race distribution: 32,235 images of Asian, 29,501 images of Caucasian, 9,110 images of black race; gender distribution: 34,044 male images and 36,802 female images; age distribution: baby, teenager, young, midlife and senior Collection environment including pure color background, indoor scenes and outdoor scenes Data diversity multiple scenes, multiple ages, multiple races, complicated expressions (closing eye, glower, pucker, opening mouth, etc.), and multiple appendages Image Parameter Data format: the image data is in .jpg or .png format, the annotation file is in .json or .psd format; the human face resolution is not lower than 128*128, and pupillary distance is not less than 60 pixels Annotation content segmentation annotation of human face, the five sense organs, body and appendages Accuracy the mask edge location errors in x and y directions are less than 3 pixels, which is considered as a qualified annotation; the annotation part (id) is regarded as the unit, the accuracy rate of segmentation annotation shall be more than 97%

Get the Dataset This is just an example of the data. To access more sample data or request the price, contact us at info@nexdata.ai

OpenForensics is the first large-scale dataset posing a high level of challenges. This dataset is designed with face-wise rich annotations explicitly for face forgery detection and segmentation. With its rich annotations, OpenForensics dataset has great potentials for research in both deepfake prevention and general human face detection. Project Page: https://sites.google.com/view/ltnghia/research/openforensics