If you are working on age-invariant face recognition or developing algorithms to predict chronological age from a single photograph, you have likely encountered the name MORPH II. But what makes this dataset so special? Why has it become a benchmark standard since its release? This article provides an exhaustive deep dive into the MORPH II dataset, its structure, its applications, and its limitations. The MORPH II dataset (often stylized as MORPH Album 2) is a large-scale, longitudinal facial image database compiled by the University of North Carolina Wilmington (UNCW) in collaboration with the National Institute of Justice (NIJ). Unlike standard datasets that collect one image per subject, MORPH II focuses on temporal variation .
| Dataset | Images | Subjects | Longitudinal? | Primary Weakness | | :--- | :--- | :--- | :--- | :--- | | | 55k | 13.6k | Yes | Demographic skew | | FG-NET | 1,002 | 82 | Yes | Very small size | | UTKFace | 20k | ~20k | No | Cross-sectional only | | IMDB-WIKI | 523k | 20k | No | Noisy labels, no longitudinal pairs | | CACD (Cross-Age) | 16k | 2k | Yes | Small subject count | morph ii dataset
The "II" signifies that it is the second major release of the MORPH database. The original MORPH (Album 1) contained approximately 1,300 subjects. MORPH II expanded this dramatically to become, for many years, the largest publicly available dataset for studying facial aging. If you are working on age-invariant face recognition