Face Parsing

The HELEN dataset is composed of 2330 face images of 400×400 pixels with labeled facial components generated through manually-annotated contours along eyes, eyebrows, nose, lips and jawline. Source: Face Parsing via a Fully-Convolutional Continuous CRF Neural Network Image Source: http://www.ifp.illinois.edu/~vuongle2/helen/

CelebAMask-HQ is a large-scale face image dataset that has 30,000 high-resolution face images selected from the CelebA dataset by following CelebA-HQ. Each image has segmentation mask of facial attributes corresponding to CelebA. Source: https://github.com/switchablenorms/CelebAMask-HQ Image Source: https://github.com/switchablenorms/CelebAMask-HQ

A large-scale Landmark guided face Parsing dataset (LaPa) for face parsing. It consists of more than 22,000 facial images with abundant variations in expression, pose and occlusion, and each image of LaPa is provided with a 11-category pixel-level label map and 106-point landmarks. Source: LaPa

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

iBugMask is an in-the-wild face parsing dataset that contains 1,000 challenging face images and manually annotated labels for 11 semantic classes: background, facial skin, left/right brow, left/right eye, nose, upper/lower lip, inner mouth, and hair. The images are curated from challenging in-the-wild face alignment datasets, including 300W and Menpo. Compared with the existing face parsing datasets, iBugMask contains in-the-wild scenarios such as “party” and “conference”, which include more challenging appearance variations or multiple faces. There is a larger number of profile faces. More expressions other than ”neutral” and ”smile” are also included (e.g. ”surprise” and ”scream”). The dataset can be downloaded on here.