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 Image Dataset: Annotated images highlighting facial landmarks across diverse races, genders, and age groups

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).

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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.

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

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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… See the full description on the dataset page: https://huggingface.co/datasets/gofixyourself/EasyPortrait.

Context

A dataset sample (4k images) of highly realistic synthetic humans, respective body-part segmentation, 2d landmarks(eyelids, pupil, nose, lips), both gaze and face orientation vector.

https://imgur.com/L7eBi9q.gif" alt="Result Preview">

Content

The dataset is divided into directories accordingly to each annotation method. Each directory contains 50000 images encoded in .PNG or .JSON for annotation.

Directories and files: - Lit - 4k .png files representing realistic renders - Segmentation - 4k .png files representing the segmentation of body-parts - Annotation - 4k .json files representing scene metadata - Resources - Dataset utility files

Protocol

Render images are 640x480 px resolution.

Following "Simulation to Real Domain Randomization" several randomization parameters are applied throughout the dataset: - Eye display material is augmented with the blend of several predefined realistic eye materials. - Following same rules, both Face and Clothing display material is randomized and augmented including texture height map, color blend from predefined colors, macro and overall normal maps, etc. - Beyond these overall scene lighting is randomized in the amount of spotlights, their location relative to the Face object and each individual spotlight color and emissive intensity. - Background is not purposely randomized.

Avoidance of cultural/ racial bias was taken into account when defining default randomization values.

In regards to metadata: For each set of images there's a correspondent metadata file (.json) which contains: - Gaze and Face orientation vector both in image space. - Key landmarks 2D coordinates.

Acknowledgments

Synthetic images were generated with UnrealEngine 3D Characters, Metahuman models

Discover Chinese Handwritten Composition Datasets Perfect for calligraphy AI, cultural studies, and linguistic research.

the dataset contain some tunnel excavation face images with segmentations

568 People - Face Detection & Face 106 Landmarks & Human Body Segmentation Annotation Data in Online Conference Scenes. The ethnic groups include East Asians, Caucasians, Blacks, and Browns, with a primary focus on young adults. Various indoor office scenes were captured, including meeting rooms, cafes, libraries, and bedrooms. In terms of annotation, each individual consists of 61 to 64 photos, with annotations for detected facial bounding boxes and 106 facial landmarks, as well as segmentation annotations for the human body. The data can be used for tasks such as facial detection, 106 facial landmark recognition, and human body segmentation.

Dataset

This dataset was created by Mr. Ri and Ms. Tique

Released under CC0: Public Domain

Contents

Get the Data

The dataset supports various deep learning applications, including facial anomaly detection, tissue segmentation, and 3D modeling of facial anatomy. With high-resolution sagittal and axial slices, it is ideal for training AI models aimed at accurate facial analysis.

💵 Access the Dataset: Access to the full dataset is available upon request. Contact us at contact@human-ai-ze.com or visit HumanAIze to discuss pricing and requirements.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F24887857%2Fdf0cb7cc972acc877c29326e2a3a99d8%2Ffcial.png?generation=1739814150106946&alt=media" alt="">

Content

The dataset includes data that showcases the diversity and complexity of facial MRI imaging, suitable for machine learning models and medical analysis. It includes:

• Sagittal and axial MRI slices: Key anatomical regions of the face.
• 3D models: Useful for volumetric and surgical planning.
• Clinical data summaries: Including information about patient demographics and scan contexts.

Medical Reports Include the Following Data:

• Type of study
• MRI machine used (e.g., Philips Ingenia 3.0T)
• Patient demographics (age, sex, medical history)
• Anamnesis (patient complaints and symptoms)
• Findings: Detailed imaging observations
• Preliminary diagnosis and clinical recommendations

All data is anonymized to ensure privacy and complies with publication consent regulations.

Potential Applications

• Anomaly detection: Facial deformities, soft tissue damage, and bone irregularities
• Segmentation models: Soft tissue, muscles, bones, and key facial landmarks
• 3D facial reconstruction: AI-powered visualization for surgery planning and diagnostics

Sample Preview

The dataset provides a sample from one patient, showcasing the diversity of the full dataset. It contains the following files for exploration:
- DICOM slices with 100 frames
- 3D representation of the facial structure
- CSV file listing the scan characteristics

🌐 HumanAIze specializes in high-quality datasets, AI/ML data curation, and annotation services. Contact us today for custom solutions tailored to your projects.

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.

Face mask segmentation mask dataset for more efficient detection and localization.

• 222 images, 222 masks.
• Original images are in "images" folder.
• Segmentation masks with the same name as the images are in "masks" images.
• If more masks are present on the image, each has a unique color in order to instance each of them (max 3 masks per image):
  • Mask 1: #ffffff
  • Mask 2: #fdeded
  • Mask 3: #fcdbdb

Contact: https://www.linkedin.com/in/pericnikola/

• Big thanks to all users on Pexels and Unsplash - find their user names in the names of the images.

• Why I made this? I was bored.

• No animals were hurt during the creation of this dataset (dataset was presented to them and they had absolutely no idea what to do with it).

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

hf-internal-testing/mask-for-image-segmentation-tests dataset hosted on Hugging Face and contributed by the HF Datasets community

Face Detection With Yolov8

## Overview

Face Detection With Yolov8 is a dataset for object detection tasks - it contains Face annotations for 3,479 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 accuracy of traditional CT image segmentation algorithms is hindered by issues such as low contrast and high noise in the images. While numerous scholars have introduced deep learning-based CT image segmentation algorithms, they still face challenges, particularly in achieving high edge accuracy and addressing pixel classification errors. To tackle these issues, this study proposes the MIS-Net (Medical Images Segment Net) model, a deep learning-based approach. The MIS-Net model incorporates multi-scale atrous convolution into the encoding and decoding structure with symmetry, enabling the comprehensive extraction of multi-scale features from CT images. This enhancement aims to improve the accuracy of lung and liver edge segmentation. In the evaluation using the COVID-19 CT Lung and Infection Segmentation dataset, the left and right lung segmentation results demonstrate that MIS-Net achieves a Dice Similarity Coefficient (DSC) of 97.61. Similarly, in the Liver Tumor Segmentation Challenge 2017 public dataset, the DSC of MIS-Net reaches 98.78.