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.
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MORPH is a facial age estimation dataset, which contains 55,134 facial images of 13,617 subjects ranging from 16 to 77 years old.
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The Adience dataset, published in 2014, contains 26,580 photos across 2,284 subjects with a binary gender label and one label from eight different age groups, partitioned into five splits. The key principle of the data set is to capture the images as close to real world conditions as possible, including all variations in appearance, pose, lighting condition and image quality, to name a few.
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The Cross-Age Celebrity Dataset (CACD) contains 163,446 images from 2,000 celebrities collected from the Internet. The images are collected from search engines using celebrity name and year (2004-2013) as keywords. Therefore, it is possible to estimate the ages of the celebrities on the images by simply subtract the birth year from the year of which the photo was taken.
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FGNet is a dataset for age estimation and face recognition across ages. It is composed of a total of 1,002 images of 82 people with age range from 0 to 69 and an age gap up to 45 years
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Contains aesthetic scores and meaningful attributes assigned to each image by multiple human raters.
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The Asian Face Age Dataset (AFAD) is a new dataset proposed for evaluating the performance of age estimation, which contains more than 160K facial images and the corresponding age and gender labels. This dataset is oriented to age estimation on Asian faces, so all the facial images are for Asian faces. It is noted that the AFAD is the biggest dataset for age estimation to date. It is well suited to evaluate how deep learning methods can be adopted for age estimation.
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AgeDB contains 16, 488 images of various famous people, such as actors/actresses, writers, scientists, politicians, etc. Every image is annotated with respect to the identity, age and gender attribute. There exist a total of 568 distinct subjects. The average number of images per subject is 29. The minimum and maximum age is 1 and 101, respectively. The average age range for each subject is 50.3 years.
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The USF Human ID Gait Challenge Dataset is a dataset of videos for gait recognition. It has videos from 122 subjects in up to 32 possible combinations of variations in factors.
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MegaAge is a large dataset that consists of 41,941 faces annotated with age posterior distributions.
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Data Description The training data contains twelve-lead ECGs. The validation and test data contains twelve-lead, six-lead, four-lead, three-lead, and two-lead ECGs:
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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.
3 PAPERS • 1 BENCHMARK
The LAGENDA dataset is a large-scale dataset with age and gender annotations for face and body bounding boxes. The dataset consists of 67,159 images from the Open Images Dataset and comprises 84,192 pairs (FaceCrop, BodyCrop). This dataset offers a high level of diversity, encompassing various scenes and domains. It contains minimal celebrity data, thus reflecting real-world, in-the-wild scenarios. The dataset spans a wide age range, from 0 to 95 years old.
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KANFace consists of 40K still images and 44K sequences (14.5M video frames in total) captured in unconstrained, real-world conditions from 1,045 subjects. The dataset is manually annotated in terms of identity, exact age, gender and kinship.
2 PAPERS • 1 BENCHMARK
EEG signals from 60 users have been recorded whose age range lies between 6 and 55 years. Among all, there were 25 females and 35 male users. In general, all the participants were either school children or belonged to the socioeconomic cross section of the population with no medical history. The EEG recordings were acquired from all 14 electrodes operating at a sampling rate of 128 Hz. During recording, the participants were asked to comfortably sit on the chair with clear thoughts and a relaxed state.
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At RSNA 2017 there was a contest to correctly identify the age of a child from an X-ray of their hand.
A large-scale, first-of-its-kind database aimed at generating a better understanding of the way children interact with mobile devices during their development process. ChildCIdbv1 comprises data collected from 438 children, from 18 months to 8 years old, encompassing the first three development stages of Piaget's theory. Data collected spans interaction with screens using both finger and pen stylus, information regarding the previous experience of the child with mobile devices, the child’s grade level, and whether attention-deficit/hyperactivity disorder (ADHD) is present.
Facial Skeletal Angles (Glabella and Maxilla Angle and Length and Width of Piriformis)
A high-resolution version of VGGFace2 for academic face editing purposes. This project uses GFPGAN for image restoration and insightface for data preprocessing (crop and align).