24 datasets found

The FaceTracer dataset contains a large collection of face images of children.

The Children Longitudinal Face (CLF) dataset contains 3,682 face images of 919 children, in the age range of 2 to 18 years. Each subject has an average of 4 images acquired over...

The ExtYaleB dataset is a face recognition dataset, containing 2,414 frontal face images of 38 subjects.

A large-scale face image dataset that contains real face images and corresponding semantic segmentation map, sketch, and textual descriptions.

The Facescrub dataset contains over 100,000 face images of 530 people.

The dataset used in this paper is a collection of 3D shapes with the same connectivity to train the network.

The FG-NET1 dataset contains 1002 images colored resolution, quality, or gray with variation in illumination, viewpoint, and expression. There are 82 different subjects with...

Face Research Lab London (FRLL) dataset used for evaluating the proposed Fast-DiM algorithm

CelebA-HQ256 dataset is a dataset of face images.

Ordinal regression refers to classifying object instances into ordinal categories. It has been widely studied in many scenarios, such as medical disease grading and movie...

The deep funneled LFW dataset contains about 13,233 images, 5,749 different identities, and 40 binary attributes for each face image.

The VGG-Face dataset contains 2.2 million URLs with corresponding face detection locations.

The CASIA-Webface dataset contains 494,414 images of 10,575 subjects.

The IARPA Janus Benchmark-B (IJB-B) dataset contains 1,026,280 face images of 1,845 individuals.

Labeled Faces in the Wild (LFW) dataset consists of 5749 subjects each having different number of images ranging from 1 to 530.

The dataset is used for kinship recognition task, where the goal is to determine if two individuals are biomedically related based solely on images of their faces.

The ABAW4 challenge includes two sub-challenges, i.e., the multi-task learning (MTL) challenge and the learning from synthetic data (LSD) challenge.

Affective behavior analysis has aroused researchers’ attention due to its broad applications. However, it is labor exhaustive to obtain accurate annotations for massive face...

The Multi-PIE dataset consists of 754,200 images from 337 subjects with large variations in head pose, illumination, and facial expression.

The dataset used in the paper is a set of images from the AFHQ dataset, containing 1.5K images of different animal faces.