Facial attribute classification is the task of classifying various attributes of a facial image - e.g. whether someone has a beard, is wearing a hat, and so on.
The UTKFace dataset is a large-scale face dataset with long age span (range from 0 to 116 years old). The dataset consists of over 20,000 face images with annotations of age, gender, and ethnicity. The images cover large variation in pose, facial expression, illumination, occlusion, resolution, etc. This dataset could be used on a variety of tasks, e.g., face detection, age estimation, age progression/regression, landmark localization, etc. Source: https://susanqq.github.io/UTKFace/ Image Source: https://susanqq.github.io/UTKFace/
FairFace is a face image dataset which is race balanced. It contains 108,501 images from 7 different race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino. Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups. Source: https://github.com/joojs/fairface
LFWA is a popular unconstrained facial attribute dataset, which consists of 13,143 facial images of 5,749 identities. Each facial image has 40 attribute annotations. Image Source: https://arxiv.org/pdf/1604.07360.pdf
CelebV-HQ is a large-scale video facial attributes dataset with annotations. CelebV-HQ contains 35,666 video clips involving 15,653 identities and 83 manually labeled facial attributes covering appearance, action, and emotion. GitHub repository: https://github.com/celebv-hq/celebv-hq
Gender-biased FFHQ dataset (bFFHQ) has age as a target label and gender as a correlated bias, and the images are from the FFHQ dataset. The images include the dominant number of young women (i.e., aged 10-29) and old men (i.e., aged 40-59) in the training data. Source: Learning Debiased Representation via Disentangled Feature Augmentation Image Source: FFHQ
A new face annotation dataset with balanced distribution between genders and ethnic origins. Source: SensitiveNets: Learning Agnostic Representations with Application to Face Images
We have cleaned the noisy IMDB-WIKI dataset using a constrained clustering method, resulting this new benchmark for in-the-wild age estimation. The annotations also allow this dataset to use for some other tasks, like gender classification and face recognition/verification. For more details, please refer to our FPAge paper.
A View From Somewhere (AVFS)—a dataset of 638,180 face similarity judgments over 4,921 faces. Each judgment corresponds to the odd-one-out (i.e., least similar) face in a triplet of faces and is accompanied by both the identifier and demographic attributes of the annotator who made the judgment.
An ancestral origin database of 14,000 images of individuals from East Asia, the Indian subcontinent, sub-Saharan Africa, and Western Europe. Source: Turning a Blind Eye: Explicit Removal of Biases and Variation from Deep Neural Network Embeddings