Generating aged or rejuvenated images for forensics or entertainment.
Because subjects appear multiple times, you must split by , not by image. If images of the same person appear in both training and test sets, your model will cheat (learning identity cues rather than age cues).
The dataset is notable for its explicit metadata annotations, including . However, the collection is heavily unbalanced, reflecting its origins in law enforcement booking data.
If you are looking to get started, I can help you: morph ii dataset
The MORPH II dataset bridging the gap between traditional geometric facial analysis and modern deep learning. It proved that deep neural networks could master the complex, non-linear patterns of human aging if given enough high-quality data.
The Morph II dataset has numerous applications in:
No discussion of the Morph II dataset is complete without addressing its . Approximately 73% of subjects are African American, and 80% are male. While this reflects the source population (volunteers from the northeastern United States recruited via drivers’ license records and community events), it raises two major concerns: Generating aged or rejuvenated images for forensics or
Researchers can use the dataset to train generative adversarial networks (GANs) or deep learning models that synthesize what a person will look like in 5 or 10 years, based on their previous facial features.
The MORPH II Dataset: A Comprehensive Overview of the Gold Standard in Facial Age Estimation
Because of its detailed race and gender labels, Morph II has been used to study in face recognition performance. Researchers have consistently found that algorithms trained on balanced datasets still perform worse on Morph II’s African American subjects when tested against models trained primarily on Caucasian faces—a finding that presaged the current fairness movement in AI. The dataset is notable for its explicit metadata
MORPH-II is notable not only for its images but also for the wealth of metadata that accompanies each entry. The file morph_2008_nonCommercial.csv contains 11 variables for each mugshot, including:
Training deep neural networks (CNNs) to predict the exact age of a person from a single photo.