Morph Ii Dataset Verified

Unlike many earlier datasets that lacked diversity, MORPH II provides a broad demographic spread, making it essential for testing algorithmic bias.

For evaluation protocols, the morph2-protocols GitHub repository provides a standardized reference.

Discrepancies in date of birth (DOB), race, and gender have been manually or algorithmically fixed. Training Readiness: morph ii dataset verified

Teaching AI to guess a person’s age within a narrow Mean Absolute Error (MAE).

: All images of a single subject are typically kept within one fold to prevent "identity leakage" (the model recognizing the person rather than learning to estimate age). Subsetting Schemes Unlike many earlier datasets that lacked diversity, MORPH

: Longitudinal tracking per subject ranging from a few months up to 5 years.

The MORPH II non-commercial release comprises real-world mugshot data with an array of accompanying biological metadata points. : 55,134 face images. Subject Count : 13,618 unique individuals. Age Distribution : Individuals aged 16 to 77 years. Training Readiness: Teaching AI to guess a person’s

: Approximately 77% Black, 19% White, and 4% Hispanic, Asian, or Native American.

While MORPH-II is a benchmark, researchers have identified that much of its raw metadata was originally , leading to inconsistencies in recorded ages or demographic data. To ensure the data is reliable for scientific use, "verified" versions or cleaning protocols have been established:

This public link is valid for 7 days and shares a thread, including any personal information you added. This link or copies made by others cannot be deleted. If you share with third parties, their policies apply. Can’t copy the link right now. Try again later. Morph - Ii Dataset Verified 2021

MORPH II DATASET (55,134 Images) │ ┌───────────────────────┴───────────────────────┐ ▼ ▼ African Descent (~77%) European Descent (~19%) │ │ ┌────────┴────────┐ ┌────────┴────────┐ ▼ ▼ ▼ ▼ Male (~67%) Female (~10%) Male (~16%) Female (~3%) Longitudinal Discrepancies