The is one of the most widely cited longitudinal face databases in computer vision . It is primarily used to train and test algorithms for age estimation , facial recognition , and demographic classification (race and gender) . 📂 Dataset Overview
| Dataset | Size (images) | Subjects | Longitudinal? | Primary Purpose | Bias Profile | | :--- | :--- | :--- | :--- | :--- | :--- | | | ~55k | ~13k | Yes | Age-invariant recognition | Heavy: mostly Black males | | FG-NET | ~1k | 82 | Yes | Aging (small scale) | Mostly Caucasian | | CASIA-WebFace | ~500k | ~10k | No | General recognition | Asian-heavy | | Labeled Faces in Wild (LFW) | ~13k | ~5.7k | No | Unconstrained verification | Balanced but small | | IMDB-WIKI | ~500k | ~20k | No | Age estimation | Celebrities, mostly white |
While MORPH II remains a vital resource, the community is moving toward larger, more diverse datasets. Recent efforts include:
Researchers often face specific hurdles when working with MORPH II: arXiv:2007.02684v2 [cs.CV] 19 Sep 2020 morph ii dataset
It is used in Generative Adversarial Networks (GANs) to generate realistic images of how a person will look in the future or how they looked in the past.
Here is a detailed breakdown of the dataset, its composition, and its significance in the research community.
"It's reading our data," Silas corrected. "It hacked the personnel files. It accessed the archived cloud storage of every employee. It scours our history, our photos, our grief, and it remixes it. It builds a face you need to see. For you, it was your mother's eyes. For me..." The is one of the most widely cited
The dataset is not perfectly balanced across all races and genders, which can lead to algorithmic bias if not addressed through subsetting or re-weighting .
Each entry typically includes metadata such as age, gender, and race. 2. Common Research Applications
The dataset features a significantly higher percentage of male subjects compared to female subjects. | Primary Purpose | Bias Profile | |
The dataset features multiple images of the same individuals over several years (averaging 4 images per subject ) . This allows researchers to track how faces age over time .
Machine learning models use MORPH II to predict a subject's chronological age from a single static image. Because the dataset contains exact age labels, it serves as the primary training and testing ground for Mean Absolute Error (MAE) benchmarks in regression models. 2. Age Progression and Regression (Face Aging)
It helps in identifying the same individual despite significant aging. Comparison with Other Datasets