What are you training for? (e.g., age estimation, GAN-based aging, cross-age verification) Which framework are you using? (e.g., PyTorch, TensorFlow)
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Typical uses
In addition to the widely used academic version, there is also a commercial version of the MORPH dataset. It is reportedly much larger, containing nearly 400,000 images of approximately 70,000 subjects, but is not available for general research purposes. morph ii dataset
Generating aged or rejuvenated images for forensics or entertainment.
| Feature | Details | | :--- | :--- | | | Longitudinal MORPH Album 2 | | Release Year | 2008 (non-commercial) | | Total Images | ~55,134 | | Unique Subjects | ~13,617 | | Age Range | 16 to 77 years | | Average Age | ~33 years | | Primary Ethnicities | ~77% African-American, ~19% Caucasian, ~4% Other | | Key Metadata | Age, Gender, Race, DOB, Capture Date | | Main Applications | Age Estimation, Face Recognition, Demographic Analysis | | Key Weaknesses | Demographic bias, metadata inconsistencies, pre-processing required |
To facilitate different research tasks, a subsetting scheme divides the full MORPH-II dataset into several standardized, pre-processed subsets, each optimized for a specific use case. These subsets help ensure that different research studies can be compared more fairly. What are you training for
For researchers evaluating models on Morph II, the following metrics are standard:
The MORPH II Dataset: A Cornerstone in Facial Age Progression and Estimation Research
If you need longitudinal pairs (same person, different ages), MORPH II is still the gold standard. If you only need age labels and don't care about matching identities, IMDB-WIKI offers more raw data. Share public link Typical uses In addition to
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Includes diverse ages (16–77 years), genders, and ethnicities (African, European, Asian, and Hispanic).