By providing these pre-defined splits, the research community can ensure that studies using MORPH-II are .
Even with verified labels, the dataset is heavily skewed toward African American males. Verified age labels do not correct for demographic sampling bias. A model trained on verified MORPH II may perform well on African American males but poorly on Caucasian females or Asian subjects. Researchers must apply reweighting or debiasing techniques separately.
: Studies like the MORPH-II Inconsistencies and Cleaning Whitepaper highlight the need to verify age and gender labels to prevent biased or inaccurate research outcomes. morph ii dataset verified
: Notable research has produced "cleaned" versions of the dataset. For instance, the MORPH-II: Inconsistencies and Cleaning Whitepaper details the creation of a "go for age" version, which removes subjects with unidentifiable birthdates to ensure consistent age information for training.
: Tracks roughly 13,000 distinct individuals over a longitudinal timeline. A model trained on verified MORPH II may
The cleaning methodology has since been adopted as a standard practice for researchers using Morph II. In 2018, a team led by Benjamin Yip proposed a for evaluation protocols, which automatically creates training and testing splits while overcoming the original unbalanced racial and gender distributions. This scheme is now widely used for gender classification, age prediction, and race classification tasks.
A diverse mix of ancestries, primarily African, European, Asian, Hispanic, and Native American. : Notable research has produced "cleaned" versions of
: Individuals changing demographic classifications across separate bookings.