Morph Ii Dataset [upd] May 2026
Introduction to Morph II Dataset
Access: [UNCW Morph Dataset Page] (Search "MORPH II dataset UNC Wilmington") morph ii dataset
One critical aspect of MORPH II is its uneven demographic balance, which researchers often manage through custom "subsetting" schemes to avoid bias. Introduction to Morph II Dataset Access: [UNCW Morph
- Gallery images: A set of 1,376 images, one per subject, used as reference images for face recognition.
- Probe images: A set of 54,000 images, consisting of multiple images per subject, used to evaluate face recognition performance.
- Morph images: A set of morphed images, generated by combining two or more face images, used to evaluate face morphing attacks.
That said, the ethical way forward is not to discard Morph II but to complement it. Researchers increasingly use Morph II for fine-tuning or validation, while relying on balanced datasets for pretraining. Some groups have also released Morph-II-rebalanced – a subset created via resampling to balance gender and ethnicity, albeit at the cost of total sample size. Gallery images : A set of 1,376 images,
The average number of images per subject is roughly 4, but some individuals have as many as 30+ images taken over several years. This dense sampling of the aging trajectory is the dataset's primary selling point.
In the academic community, MORPH II is frequently used as a benchmark to compare the performance of various neural networks. Whether it is a Convolutional Neural Network (CNN) or a more modern Transformer-based architecture, the "Mean Absolute Error" (MAE) in years is the typical metric used to judge success. Over the last decade, the MAE on MORPH II has dropped significantly, moving from errors of five or six years down to less than three years in some state-of-the-art implementations. This progress highlights the dataset's role in driving the evolution of facial analysis technology.
