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SCUT-FBP5500
Classification
Face
|...
License: Custom

Overview

The SCUT-FBP5500 dataset has totally 5500 frontal faces with diverse properties (male/female,
Asian/Caucasian, ages) and diverse labels (facial landmarks, beauty scores in 5 scales, beauty
score distribution), which allows different computational model with different facial beauty
prediction paradigms, such as appearance-based/shape-based facial beauty classification/regression/ranking
model for male/female of Asian/Caucasian.

The SCUT-FBP5500 Dataset can be divided into four
subsets with different races and gender, including 2000 Asian females(AF), 2000 Asian males(AM),
750 Caucasian females(CF) and 750 Caucasian males(CM). Most of the images of the SCUT-FBP5500
were collected from Internet, where some portions of Asian faces were from the DataTang, GuangZhouXiangSu
and our laboratory, and some Caucasian faces were from the 10k US Adult Faces database. image

All the images are labeled with beauty scores ranging from [1, 5] by totally 60 volunteers,
and 86 facial landmarks are also located to the significant facial components of each images.
Specifically, we save the facial landmarks in ‘pts’ format, which can be converted to 'txt'
format by running pts2txt.py. We developed several web-based GUI systems to obtain the facial
beauty scores and facial landmark locations, respectively.

Citation

@article{liang2017SCUT,
  title     = {SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction},
  author    = {Liang, Lingyu and Lin, Luojun and Jin, Lianwen and Xie, Duorui and Li, Mengru},
  jurnal    = {ICPR},
  year      = {2018}
}

License

Custom

Data Summary
Type
Image,
Amount
7.7K
Size
171.6MB
Provided by
DLVC lab
Deep Learning and Vision Computing Lab, SCUTHCIILAB
| Amount 7.7K | Size 171.6MB
SCUT-FBP5500
Classification
Face
License: Custom

Overview

The SCUT-FBP5500 dataset has totally 5500 frontal faces with diverse properties (male/female,
Asian/Caucasian, ages) and diverse labels (facial landmarks, beauty scores in 5 scales, beauty
score distribution), which allows different computational model with different facial beauty
prediction paradigms, such as appearance-based/shape-based facial beauty classification/regression/ranking
model for male/female of Asian/Caucasian.

The SCUT-FBP5500 Dataset can be divided into four
subsets with different races and gender, including 2000 Asian females(AF), 2000 Asian males(AM),
750 Caucasian females(CF) and 750 Caucasian males(CM). Most of the images of the SCUT-FBP5500
were collected from Internet, where some portions of Asian faces were from the DataTang, GuangZhouXiangSu
and our laboratory, and some Caucasian faces were from the 10k US Adult Faces database. image

All the images are labeled with beauty scores ranging from [1, 5] by totally 60 volunteers,
and 86 facial landmarks are also located to the significant facial components of each images.
Specifically, we save the facial landmarks in ‘pts’ format, which can be converted to 'txt'
format by running pts2txt.py. We developed several web-based GUI systems to obtain the facial
beauty scores and facial landmark locations, respectively.

Citation

@article{liang2017SCUT,
  title     = {SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction},
  author    = {Liang, Lingyu and Lin, Luojun and Jin, Lianwen and Xie, Duorui and Li, Mengru},
  jurnal    = {ICPR},
  year      = {2018}
}

License

Custom

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