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Facial Color Segmentation Dataset

The progress of video and photo editing applications relies heavily on high-quality datasets for machine learning models training. Our carefully selected datasets play a crucial role in enhancing the abilities of such applications, by offering accurate segmentation and recognition of different elements within images and videos.

The "Facial Color Segmentation Dataset" is tailored for the beauty and visual entertainment sectors, consisting of internet-collected images with resolutions from 1028 x 1028 to 6016 x 4016 pixels. This dataset focuses on semantic segmentation of facial skin colors, including black, yellow, white, and brown, facilitating diverse applications in cosmetics, virtual makeovers, and inclusive digital content.

Sample

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Specification

Dataset ID
MD-Image-025
Dataset Name
Facial Color Segmentation Dataset
Data Type
data-typeImage
Volume
About 3.9k
Data Collection
Internet collected images. Resolution ranges from 1028 x 1028 to 6016 x 4016 pixels.
Annotation
Semantic Segmentation
Annotation Notes
The annotation includes black, yellow, white and brown skin.
Application Scenarios
Beauty;Visual Entertainment

Data Collections

Comprising approximately 3.9k images, this dataset offers a focused examination of facial skin tones, providing annotations for various skin colors. The detailed segmentation supports the development of personalized beauty apps and visual effects in entertainment, enhancing user experience with realistic and diverse representations of skin tones in digital environments.

Quality Assurance

Quality Assurance

Relevant Open Datasets

To supplement our Face Parsing Dataset, users can explore these open datasets for additional resources:

FASSEG Repository [Learn more]

This collection offers datasets for frontal face segmentation (Frontal01 and Frontal02) and a dataset for faces in multiple poses (Multipose01).These datasets can be valuable for training models to perform face segmentation in different orientations and conditions.

Related products

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