IMDB-WIKI
Classification
Face
|...
许可协议: Custom

Overview

we took the list of the most popular 100,000 actors as listed on the IMDb website and (automatically) crawled from their profiles date of birth, name, gender and all images related to that person. Additionally we crawled all profile images from pages of people from Wikipedia with the same meta information. We removed the images without timestamp (the date when the photo was taken). Assuming that the images with single faces are likely to show the actor and that the timestamp and date of birth are correct, we were able to assign to each such image the biological (real) age. Of course, we can not vouch for the accuracy of the assigned age information. Besides wrong timestamps, many images are stills from movies - movies that can have extended production times. In total we obtained 460,723 face images from 20,284 celebrities from IMDb and 62,328 from Wikipedia, thus 523,051 in total.

Instruction

For both the IMDb and Wikipedia images we provide a separate .mat file which can be loaded with Matlab containing all the meta information. The format is as follows:

  • dob: date of birth (Matlab serial date number)

  • photo_taken: year when the photo was taken

  • full_path: path to file

  • gender: 0 for female and 1 for male, NaN if unknown

  • name: name of the celebrity

  • face_location:

    location of the face. To crop the face in Matlab run

    img(face_location(2):face_location(4),face_location(1):face_location(3),:))
    
  • face_score: detector score (the higher the better). Inf implies that no face was found in the image and the face_location then just returns the entire image

  • second_face_score: detector score of the face with the second highest score. This is useful to ignore images with more than one face. second_face_score is NaN if no second face was detected.

  • celeb_names (IMDB only): list of all celebrity names

  • celeb_id (IMDB only): index of celebrity name

The age of a person can be calculated based on the date of birth and the time when the photo was taken (note that we assume that the photo was taken in the middle of the year):

[age,~]=datevec(datenum(wiki.photo_taken,7,1)-wiki.dob);

Citation

Please add a reference if you are using the dataset or the pretrained models.

@article{Rothe-IJCV-2018,
  author = {Rasmus Rothe and Radu Timofte and Luc Van Gool},
  title = {Deep expectation of real and apparent age from a single image without facial landmarks},
  journal = {International Journal of Computer Vision},
  volume={126},
  number={2-4},
  pages={144--157},
  year={2018},
  publisher={Springer}
}
@InProceedings{Rothe-ICCVW-2015,
  author = {Rasmus Rothe and Radu Timofte and Luc Van Gool},
  title = {DEX: Deep EXpectation of apparent age from a single image},
  booktitle = {IEEE International Conference on Computer Vision Workshops (ICCVW)},
  year = {2015},
  month = {December},
}

License

Custom

数据概要
数据格式
Image,
数据量
523.051K
文件大小
276.23GB
发布方
ETH zurish
The computer vision lab performs research in the fields of Medical Image Analysis and Visualization, Object Recognition, Gesture Analysis, Tracking, and Scene Understanding and Modeling.
数据集反馈
| 230 | 数据量 523.051K | 大小 276.23GB
IMDB-WIKI
Classification
Face
许可协议: Custom

Overview

we took the list of the most popular 100,000 actors as listed on the IMDb website and (automatically) crawled from their profiles date of birth, name, gender and all images related to that person. Additionally we crawled all profile images from pages of people from Wikipedia with the same meta information. We removed the images without timestamp (the date when the photo was taken). Assuming that the images with single faces are likely to show the actor and that the timestamp and date of birth are correct, we were able to assign to each such image the biological (real) age. Of course, we can not vouch for the accuracy of the assigned age information. Besides wrong timestamps, many images are stills from movies - movies that can have extended production times. In total we obtained 460,723 face images from 20,284 celebrities from IMDb and 62,328 from Wikipedia, thus 523,051 in total.

Instruction

For both the IMDb and Wikipedia images we provide a separate .mat file which can be loaded with Matlab containing all the meta information. The format is as follows:

  • dob: date of birth (Matlab serial date number)

  • photo_taken: year when the photo was taken

  • full_path: path to file

  • gender: 0 for female and 1 for male, NaN if unknown

  • name: name of the celebrity

  • face_location:

    location of the face. To crop the face in Matlab run

    img(face_location(2):face_location(4),face_location(1):face_location(3),:))
    
  • face_score: detector score (the higher the better). Inf implies that no face was found in the image and the face_location then just returns the entire image

  • second_face_score: detector score of the face with the second highest score. This is useful to ignore images with more than one face. second_face_score is NaN if no second face was detected.

  • celeb_names (IMDB only): list of all celebrity names

  • celeb_id (IMDB only): index of celebrity name

The age of a person can be calculated based on the date of birth and the time when the photo was taken (note that we assume that the photo was taken in the middle of the year):

[age,~]=datevec(datenum(wiki.photo_taken,7,1)-wiki.dob);

Citation

Please add a reference if you are using the dataset or the pretrained models.

@article{Rothe-IJCV-2018,
  author = {Rasmus Rothe and Radu Timofte and Luc Van Gool},
  title = {Deep expectation of real and apparent age from a single image without facial landmarks},
  journal = {International Journal of Computer Vision},
  volume={126},
  number={2-4},
  pages={144--157},
  year={2018},
  publisher={Springer}
}
@InProceedings{Rothe-ICCVW-2015,
  author = {Rasmus Rothe and Radu Timofte and Luc Van Gool},
  title = {DEX: Deep EXpectation of apparent age from a single image},
  booktitle = {IEEE International Conference on Computer Vision Workshops (ICCVW)},
  year = {2015},
  month = {December},
}

License

Custom

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