: The full dataset is maintained by the Face Aging Group at the University of North Carolina Wilmington (UNCW) . You must typically apply for access as it requires a license for non-commercial or commercial use.
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. morph ii dataset
: Largely consists of Black (approx. 77%) and White (approx. 19%) individuals, with a significant male majority. 🛠️ Content Development Workflow : The full dataset is maintained by the
MORPH II is a longitudinal dataset, meaning it contains multiple images of the same subjects taken at different points in time. This temporal aspect makes it invaluable for studying how faces change with age. Whether it is a Convolutional Neural Network (CNN)
Because MORPH II includes race and gender labels, it has become a standard tool for auditing algorithmic fairness. Studies consistently show that age estimation algorithms perform differently across demographic groups (e.g., higher error rates for older subjects or minority groups). Researchers use MORPH II to measure and mitigate these biases.