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CACD
Classification
Face
|...
License: Custom

Overview

We propose a novel coding framework called Cross-Age Reference Coding (CARC). By leveraging
a large-scale image dataset freely available on the Internet as a reference set, CARC is able
to encode the low-level feature of a face image with an age-invariant reference space. In the
testing phase, the proposed method only requires a linear projection to encode the feature
and therefore it is highly scalable. To thoroughly evaluate our work, we introduce a new large-scale
dataset for face recognition and retrieval across age called Cross-Age Celebrity Dataset (CACD).
The dataset contains more than 160,000 images of 2,000 celebrities with age ranging from 16
to 62. To the best of our knowledge, it is by far the largest publicly available cross-age
face dataset. Experimental results show that the proposed method can achieve state-of-the-art
performance on both our dataset as well as the other widely used dataset for face recognition
across age, MORPH dataset.

Data Annotation

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. We can therefore estimate the ages of the celebrities on the images
by simply subtract the birth year from the year of which the photo was taken. The downloaded
dataset contain two MATLAB structures:

  • celebrityData - contains information of the 2,000 celebrities

    • name - celebrity name
    • identity - celebrity id
    • birth - celebrity brith year
    • rank - rank of the celebrity with same birth year in IMDB.com when the dataset was constructed
    • lfw - whether the celebrity is in LFW dataset
  • celebrityImageData - contains information of the face images

  • age - estimated age of the celebrity

    • identity - celebrity id
    • year - estimated year of which the photo was taken
    • feature - 75,520 dimension LBP feature extracted from 16 facial landmarks
    • name - file name of the image

Citation

@inproceedings{chen14cross,
Author = {Bor-Chun Chen and Chu-Song Chen and Winston H. Hsu},
Booktitle = {Proceedings of the European Conference on Computer Vision ({ECCV})},
Title = {Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval},
Year = {2014}
}

License

Custom

Data Summary
Type
Image,
Amount
163.446K
Size
7.83GB
Provided by
Institute of Information Science, Academia Sinica, Taipei, Taiwan
The Institute of Information Science (IIS) was formally established in September 1982 after a five-year preparation period, and is one of the eleven institutes and research centers within the Division of Mathematical and Physical Sciences. National Taiwan University is a national university in Taipei City, Taiwan. NTU is the most prestigious comprehensive university in Taiwan and one of the top-ranked universities in Asia.
| Amount 163.446K | Size 7.83GB
CACD
Classification
Face
License: Custom

Overview

We propose a novel coding framework called Cross-Age Reference Coding (CARC). By leveraging
a large-scale image dataset freely available on the Internet as a reference set, CARC is able
to encode the low-level feature of a face image with an age-invariant reference space. In the
testing phase, the proposed method only requires a linear projection to encode the feature
and therefore it is highly scalable. To thoroughly evaluate our work, we introduce a new large-scale
dataset for face recognition and retrieval across age called Cross-Age Celebrity Dataset (CACD).
The dataset contains more than 160,000 images of 2,000 celebrities with age ranging from 16
to 62. To the best of our knowledge, it is by far the largest publicly available cross-age
face dataset. Experimental results show that the proposed method can achieve state-of-the-art
performance on both our dataset as well as the other widely used dataset for face recognition
across age, MORPH dataset.

Data Annotation

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. We can therefore estimate the ages of the celebrities on the images
by simply subtract the birth year from the year of which the photo was taken. The downloaded
dataset contain two MATLAB structures:

  • celebrityData - contains information of the 2,000 celebrities

    • name - celebrity name
    • identity - celebrity id
    • birth - celebrity brith year
    • rank - rank of the celebrity with same birth year in IMDB.com when the dataset was constructed
    • lfw - whether the celebrity is in LFW dataset
  • celebrityImageData - contains information of the face images

  • age - estimated age of the celebrity

    • identity - celebrity id
    • year - estimated year of which the photo was taken
    • feature - 75,520 dimension LBP feature extracted from 16 facial landmarks
    • name - file name of the image

Citation

@inproceedings{chen14cross,
Author = {Bor-Chun Chen and Chu-Song Chen and Winston H. Hsu},
Booktitle = {Proceedings of the European Conference on Computer Vision ({ECCV})},
Title = {Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval},
Year = {2014}
}

License

Custom

0
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