The facility, a sprawling, sun-bleached complex of concrete and rebar, was quieter than she remembered. The "Morpheus Project" had been a defense grant darling a decade ago—aimed at creating deep-fake detection algorithms. The goal was noble: build a database of manipulated media so sophisticated that AI could learn to spot the fakes. The Morph I dataset had been crude—obvious face-swaps, glitchy audio.
That said, the ethical way forward is not to discard Morph II but to . 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.
It hadn't been taken by a security camera. morph ii dataset
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 .
The (often stylized as MORPH-II) is a large-scale, longitudinal dataset of facial images primarily designed for research on age progression and face recognition across time . Unlike static datasets that capture a single image per subject, Morph II contains multiple images of the same individuals taken over periods ranging from months to several years. The facility, a sprawling, sun-bleached complex of concrete
Crucially, MORPH II is composed of mugshot-style images collected from real-world law enforcement systems. This real-world origin gives it an ecological validity that synthetic or studio-controlled datasets lack.
However, for reproducible, benchmark-driven research in age estimation and longitudinal recognition, . Its combination of scale, longitudinal depth, and real-world capture conditions has not yet been fully surpassed by any publicly available alternative. The Morph I dataset had been crude—obvious face-swaps,
: The non-commercial version of the dataset contains 55,134 images of approximately 13,000 different individuals.