Morph Ii Dataset Verified

Every image is linked to a unique subject ID that has been manually or algorithmically verified to ensure no "identity leakage" (where different IDs are actually the same person) occurs.

The verified dataset yields a finalized, clean CSV file detailing the exact, authenticated parameters for every single remaining image. This ensures that any two labs running an experiment on the verified set are using the exact same data points. Key Research Applications of the Verified Dataset

The verification process generally involves the following pipeline: Step 1: Algorithmic Identity Deduplication

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with labels already provided in CSV format for immediate use in machine learning. Recent "Interesting" Applications Morphing Attack Detection (MAD)

In raw sets, some individuals arrested multiple times logged conflicting birth years, leading to impossible age-progression labels. A verified set rectifies these mathematical anomalies to ensure the ground-truth age labels are perfectly sequential. 2. Mislabeled Gender and Race Data

Cross-referencing subject IDs with chronological age progressions to flag impossible age jumps (e.g., aging 20 years in a 2-year span). Correcting incorrectly labeled gender and ethnicity tags. Removing duplicated or heavily corrupted images. 2. Standardized Partitioning Every image is linked to a unique subject

: Researchers use standardized "verified" splits (protocols) to benchmark algorithms for age estimation, ensuring results are comparable across different studies. Morph Attack Detection (MAD)

: Images were often captured in real-world, uncontrolled conditions, offering a variety of facial expressions and backgrounds. Data Verification and "Cleaning"

As one research paper noted, prior to verification, some studies reported the total number of subjects as 13,618 when it was actually 13,617, or misclassified gender categories. While seemingly minor, these errors indicated that the foundational data had not been properly cleaned. Key Research Applications of the Verified Dataset The

Contains approximately 55,134 images of about 13,000 individuals .

The study identified a total of across various categories, including 1,906 subjects with multiple possible birthdates, 33 cases where race was "too difficult to tell," and 2 cases where race changed more than once.

Initial results: Model A reports MAE of 2.8 years. Model B reports MAE of 3.1 years. At first glance, Model A appears superior. However, when tested on a completely fresh holdout set of real-world webcam images, Model A’s MAE jumps to 4.5 years (overfitting to noise), while Model B maintains a stable 3.2 years MAE.

Many practical applications consider the dataset "verified" for use when models achieve a CS where roughly 81% of images are predicted with an error of less than 5 years. Key Performance Indicators