EasyPortrait

Face Parsing and Portrait Segmentation Dataset

ImagesCC-AS-4.0Introduced 2023-04-26

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 with high quality annotated masks. This dataset is divided into training set, validation set and test set by hashed subject user_id. The training set includes 14000 images, the validation set includes 2000 images, and the test set includes 4000 images.

Training images were received from 5,947 unique users, while validation was from 860 and testing was from 1,570. On average, each EasyPortrait image has 254 polygon points, from which it can be concluded that the annotation is of high quality. Segmentation masks were created from polygons for each annotation.

Annotations are presented as 2D-arrays, images in *.png format with several classes:

| Index | Class | |------:|:-----------| | 0 | BACKGROUND | | 1 | PERSON | | 2 | SKIN | | 3 | LEFT BROW | | 4 | RIGHT_BROW | | 5 | LEFT_EYE | | 6 | RIGHT_EYE | | 7 | LIPS | | 8 | TEETH |

Also, we provide some additional meta-information for dataset in annotations/meta.zip file:

| | attachment_id | user_id | data_hash | width | height | brightness | train | test | valid | |---:|:--------------|:--------|:----------|------:|-------:|-----------:|:------|:------|:------| | 0 | de81cc1c-... | 1b... | e8f... | 1440 | 1920 | 136 | True | False | False | | 1 | 3c0cec5a-... | 64... | df5... | 1440 | 1920 | 148 | False | False | True | | 2 | d17ca986-... | cf... | a69... | 1920 | 1080 | 140 | False | True | False |

where:

  • attachment_id - image file name without extension
  • user_id - unique anonymized user ID
  • data_hash - image hash by using Perceptual hashing
  • width - image width
  • height - image height
  • brightness - image brightness
  • train, test, valid are the binary columns for train / test / val subsets respectively