A unique identifier to track the same person across different years.
Elara stared at the screen. The "son" smiled, and the warmth of it radiated through the glass, tempting her. It was a siren song of pixels.
One critical aspect of MORPH II is its uneven demographic balance, which researchers often manage through custom "subsetting" schemes to avoid bias. morph ii dataset
: Heavily male-dominant, with a male-to-female ratio of roughly 5.5:1.
[UNCW Morph Dataset Page] (Search "MORPH II dataset UNC Wilmington") A unique identifier to track the same person
MORPH-II comprises of 13,617 unique individuals . The photographs were taken between 2003 and late 2007. This longitudinal span—while only about five years long—is valuable because it captures repeat offenders who were arrested multiple times during that period, providing a longitudinal aspect that is rare in publicly available face databases. On average, there are approximately four images per subject , though some individuals appear many more times, allowing for effective observation of facial changes over time.
The MORPH II dataset offers several benefits, including: It was a siren song of pixels
The dataset includes a diverse range of subjects across different ethnicities, including African, European, Asian, and Hispanic. Age Range: Subjects range from 16 to 77 years old Attributes:
The dataset is frequently used to train classifiers to distinguish between male and female subjects. Face Recognition & Aging:
: Use libraries like OpenCV or Dlib to detect and crop faces to reduce background noise.
Unlike synthetic datasets generated by artificial intelligence, MORPH II consists of real-world operational images. The database contains over collected from nearly 13,000 unique individuals . This scale provides the statistical power needed to train and validate deep learning models, such as Convolutional Neural Networks (CNNs), which require large volumes of data to achieve high accuracy. Key Statistics and Demographic Breakdown