Researchers must sign a Data Use Agreement (DUA) ensuring the data is used for non-commercial, academic research only.
The dataset comprises over 55,000 images of more than 13,000 individuals. What distinguishes Morph II from other facial databases is the temporal distribution. The images were taken over a span of decades, with the average time lapse between the earliest and latest image of a single individual being significant enough to exhibit visible aging. The subjects range in age from 16 to 77, capturing the critical transitions from young adulthood to middle and late adulthood. Crucially, the dataset includes metadata such as age, gender, and race, allowing for nuanced analysis of how aging differs across demographics.
Discrepancies in date of birth (DOB), race, and gender have been manually or algorithmically fixed. Training Readiness:
The was created by the Face Aging Group at the University of North Carolina Wilmington. The Album 2 (MORPH II) is the large-scale longitudinal version of this project. Unlike static datasets, MORPH II focuses on the "metamorphosis" of the human face over time. morph ii dataset verified
The term "verified" in the context of MORPH II typically refers to the 2008 non-commercial release
The verification steps focused on several critical areas:
: Popular schemes involve balanced subsets, such as 9,600 images equally divided among Black/White Males and Females. How to Access While versions of the dataset exist on platforms like Researchers must sign a Data Use Agreement (DUA)
These images are mugshots taken between 2003 and late 2007. Because the subjects were arrested multiple times over this period, the dataset captures natural without relying on synthetic aging algorithms, with the average individual having about four images in the set. The ages of subjects range from 16 to 77 years, and the images include pose, lighting, expression variations, and even occlusions , making it a rich testing ground for algorithms that must function in real-world conditions.
The stands as a cornerstone in the field of forensic science and biometric identification, representing one of the most comprehensive and rigorously compiled collections of facial images designed specifically for studying the phenomenon of facial aging. As biometric systems became ubiquitous in security, law enforcement, and identity verification during the early 21st century, a critical vulnerability emerged: these systems often struggled to recognize individuals over time. The human face is not a static entity; it is dynamic, subject to the relentless forces of biological growth, gravity, and lifestyle factors. The Morph II dataset was created to address this "temporal drift," providing researchers with a robust tool to train and test algorithms capable of recognizing faces across significant time spans.
For further detailed statistics, you can access the MORPH Non-Commercial Release Whitepaper provided by the official research team. arXiv:2007.02684v2 [cs.CV] 19 Sep 2020 The images were taken over a span of
While the original dataset is popular, researchers have identified "interesting" inconsistencies—such as self-reported age and gender errors. This has led to the creation of verified subsets University of North Carolina Wilmington | UNCW MORPH-II Inconsistencies and Cleaning : A notable whitepaper from details the process of correcting these errors. MORPH Subgroups and Cleaning : Available on
If you want, I can: (a) produce scripts (data splits, pair generation, evaluation), (b) generate a reproducible experiment config, or (c) create tables of sample metrics and templates for reporting. Which do you want?
The goal is to “minimize image noise by the use of bounding boxes around necessary region of interest (ROI)”. This preprocessing ensures that subsequent experiments—whether for age estimation, gender classification, or face recognition—are based on consistent, high-quality facial images.