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.
A script verifies the delta (difference in time) between a subject’s photos. If Photo A was taken 730 days before Photo B, the age metadata must reflect a two-year increase. Any image failing this strict chronological continuity check is either corrected or purged. Step 3: Face Alignment and Quality Filtering morph ii dataset verified
Standardized splits for training and testing (80-10-10) are commonly used to benchmark results in facial age estimation. specific algorithms used to clean these datasets or how to implement the training protocols in Python? arXiv:2007.02684v2 [cs.CV] 19 Sep 2020 The was created by the Face Aging Group
: Verified versions often use specific training/testing splits (such as 80-10-10 or 80-20) and automated subsetting schemes to balance racial and gender distributions. A script verifies the delta (difference in time)
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