MORPH-II is the second and largest release of the (Metropolitan Interchange on Reconstructive Progression of High-resolution) project. It contains approximately 55,134 images from 13,618 individuals , with longitudinal spans ranging from a few days to over twenty years.
However, researchers must of MORPH-II. This means:
The integrity of AI models relies entirely on the quality of the training data. An "unverified" or uncleaned dataset can introduce biases, leading to poor model generalization. 1. Cleaning and Inconsistency Removal
Research teams at UNC Wilmington and other institutions have published "cleaning" strategies to correct these inconsistencies. morph ii dataset verified
The verification of MORPH II has paved the way for advanced derivative datasets. One notable example is the , derived directly from the verified MORPH II images. Recognizing the threat of "face morph attacks" (where images of two people are blended to create an ID that both can use), researchers created the MorphAge dataset to study how aging affects this vulnerability. The dataset is split into two bins: one with age variation of 1-2 years and another with variations of 2-5 years.
Despite its popularity, MORPH-II is . In a 2018 study, Yip et al. systematically examined the dataset and found inconsistencies in records of subjects’ age, gender, and race—issues that had not been acknowledged in prior research. For example:
The dataset includes natural variations in lighting, facial hair, weight gain/loss, and minor pose shifts. MORPH-II is the second and largest release of
: Contains approximately 55,134 unique facial images.
use MORPH-II as a "non-synthetic" baseline to compare against high-quality GAN-generated faces. used to clean this data or how to gain access to the official non-commercial version? arXiv:2007.02684v2 [cs.CV] 19 Sep 2020
Because the original metadata relied on self-reported booking data from local police departments, it suffered from human error. Academic teams published data-cleaning whitepapers to isolate a subset, correcting the following errors: This means: The integrity of AI models relies
While widely used, the "verified" status often refers to academic cleaning efforts that have corrected inherent data inconsistencies.
The uncleaned MORPH II commercial and non-commercial releases contain 55,134 unique mugshots captured from more than 13,000 distinct individuals between 2003 and 2007.
The represents the gold standard for longitudinal face analysis research. Through rigorous cleaning, careful subsetting, and standardized evaluation protocols, it has evolved from a raw collection of mugshots into a trusted benchmark for age estimation, gender and race classification, and facial recognition.
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.