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: A simple 80/20 training/testing split, though it is often criticized for lack of reproducibility. official application process to obtain the MORPH II dataset for a research project? AI responses may include mistakes. Learn more arXiv:2007.02684v2 [cs.CV] 19 Sep 2020
Unlike laboratory-controlled datasets (e.g., FERET, FG-NET), MORPH II comprises images collected from actual mug shot booking systems. As of its final release (Album 2, released around 2007–2008), MORPH II contains approximately from over 13,000 subjects , with ages ranging from 16 to 77 years. Each subject has multiple images (an average of ~4 images per person) captured over a span of weeks to years, allowing for the modeling of intra-subject facial aging.
Developed by the , MORPH is a longitudinal dataset containing over 55,000 images from more than 13,000 individual subjects. The images span a wide age range—from 16 to 77 years old—making it a uniquely valuable resource for tracking how human faces change over significant periods. morph ii dataset verified
: Although the data is sourced from real mugshots, a notable whitepaper, "MORPH-II: Inconsistencies and Cleaning,"
It contains images of both male and female subjects. : A simple 80/20 training/testing split, though it
Despite its scientific utility, the Morph II dataset is not without controversy. The source of the images—criminal arrest records—raises ethical questions regarding consent and privacy. Unlike datasets collected in a university setting where subjects volunteer, the individuals in Morph II did not consent to their mugshots being used for research. This is a common tension in forensic research: the necessity of using "real-world" data versus the rights of the subjects. Furthermore, the demographic composition, while diverse, is not perfectly balanced. The dataset skews heavily male, reflecting the demographics of the correctional system, which can impact the training of models if not carefully weighted.
This imbalance is a recurring challenge for researchers. Models trained on MORPH-II may inadvertently learn demographic biases, and evaluation protocols must account for these imbalances to ensure fair performance reporting. Learn more arXiv:2007
Human entry errors during data collection resulted in a small percentage of subjects being assigned different biological sexes or ethnic identifiers across different photo sessions. Verification pipelines audit the metadata to enforce identity continuity. 3. Unbalanced Demographic Folds
To understand the need for verification, it helps to first know the dataset’s composition. MORPH-II consists of mugshots captured under relatively controlled conditions: subjects stand in front of a neutral background, roughly the same distance from the camera, with flash illuminating the face. However, there is still considerable variation in head tilt, camera distance, illumination, occlusion, hairstyle, and makeup—especially for female subjects, who show increased variation.
One of the most critical contributions of the verified Morph II dataset is its use in . Because the dataset includes metadata for race and gender, researchers can evaluate how algorithms perform across different demographic groups.
, it contains over 55,000 images of more than 13,000 unique subjects, captured between 2003 and 2007. Core Attributes and Composition
