Morph Ii Dataset |link| < 2025 >
The drive from Berkeley to the facility in the Sierra foothills usually took two hours. Today, it took Dr. Elara Vance seven. She stopped twice to vomit on the side of Highway 49, not from a virus, but from the sheer, vibrating frequency of the denial rattling inside her chest.
Note:
There is no public, unauthenticated download link. Be wary of third-party sites claiming to host MORPH II—they are likely violating the license terms and may distribute corrupted or mislabeled data. morph ii dataset
- Use both regression (MAE, RMSE) and classification (accuracy by age-bin) metrics for age tasks.
- For recognition across age gaps, report verification TAR/FAR at multiple thresholds and stratify by age-gap bins.
- Perform cross-dataset testing (train on MORPH-II, test on other age datasets) to measure generalization.
MAE (Mean Absolute Error)
MORPH II is the primary benchmark for in age estimation. Researchers use it to train models that can predict a person’s age within a narrow margin (the current state-of-the-art often achieves an MAE of under 3 years). 2. Cross-Age Face Recognition The drive from Berkeley to the facility in
Introduction
State-of-the-art results as of 2024–2025: Use both regression (MAE, RMSE) and classification (accuracy
- Identification: Evaluate the performance of face recognition systems in identifying individuals.
- Verification: Evaluate the performance of face recognition systems in verifying the identity of individuals.
- Morphing attack detection: Evaluate the performance of face morphing attack detection algorithms.
Controlled Environment:
As a mugshot database, the photos generally follow a standard format (frontal view, neutral expression), though variations in head tilt, illumination, and camera distance still exist .