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Feb 10, 2022 7:38 AM


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 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


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}
Data Preview
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🎉Many thanks to Graviti Open Datasets for contributing the dataset
Basic Information
Application ScenariosFace
TasksNot Available
Updated on2022-02-10 07:38:10
Data TypeImage
Data Volume163,446
Annotation Amount163,446
File Size3.49GB
Copyright Owner
Institute of Information Science, Academia Sinica, Taipei, Taiwan