Morph Ii Dataset Verified Jun 2026

MORPH-II is the second and largest release of the MORPH (Metropolitan Interchange on Reconstructive Progression of High-resolution) project. It contains approximately 55,134 images from 13,618 individuals , with longitudinal spans ranging from a few days to over twenty years. Demographics: The database includes metadata for age, gender, and ethnicity (primarily European and African, with smaller subsets for Asian and Hispanic). Applications: It is primarily utilized to address age-related challenges in facial recognition and for training deep learning models in demographic classification. Proposed Subsetting and Verification Schemes Researchers have proposed various schemes to "verify" and improve the dataset's reliability for training, addressing its inherent racial and gender imbalances: Independence Schemes: A common verification protocol involves ensuring absolute independence between training and testing sets to prevent "data leakage". Racial/Gender Balancing: Specific subsetting schemes have been designed to create more uniform distributions, allowing for better generalization in age prediction and race classification tasks. Synthetic Verification: Newer methods use synthetic face morphing datasets (like the one proposed in 2024 with 2,450 identities) to benchmark against MORPH-II, verifying the vulnerability of face recognition systems to sophisticated morphing attacks. Performance Benchmarks on MORPH-II MORPH-II serves as a standard benchmark for evaluating the Mean Absolute Error (MAE) and Cumulative Score (CS) of age estimation algorithms. State-of-the-Art (SOTA): Recent models, such as the Semantic Attention Guided Hierarchical Decision Network , have achieved MAEs as low as 2.18 on this dataset. Error Rates: 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

The MORPH II dataset, developed by the University of North Carolina Wilmington (UNCW), is the world's largest longitudinal facial recognition database, containing over 55,000 unique images from roughly 13,000 subjects . It is a cornerstone for research in facial aging, age estimation, and demographic classification. Dataset Overview and Composition Collected between 2003 and 2007, MORPH II provides a critical longitudinal perspective, capturing subjects multiple times over a five-year span. Demographics : The dataset includes male and female subjects from diverse ethnic backgrounds, primarily African and European, with some Asian and Hispanic representation. Age Range : Subjects range from 16 to 77 years old . Metadata : Each image is accompanied by extensive metadata, including age, sex, and race. Environmental Factors : Images were often captured in real-world, uncontrolled conditions, offering a variety of facial expressions and backgrounds. Data Verification and "Cleaning" While widely cited, researchers have identified inconsistencies in the original raw MORPH II data, leading to "verified" or "cleaned" subsets. Self-Reported Inconsistencies : Much of the original mugshot data was self-reported, leading to errors in recorded birthdates and ages. Cleaning Strategies : Researchers at UNCW and other institutions have published whitepapers detailing steps to "clean" the data, such as resolving date conflicts to ensure accurate longitudinal analysis. Standardized Protocols : To ensure results are comparable across different studies, researchers use specific facial age estimation protocols like the RANDOM (80/20 split), WHOLE , and AGR protocols. Key Research Applications (PDF) Preliminary Studies on a Large Face Database - ResearchGate

The MORPH-II dataset is one of the most widely recognized longitudinal face databases used for research in facial age estimation, gender classification, and race recognition. Created by Ricanek and Tesafaye, it was developed to address the limitations of smaller datasets by providing a massive corpus of images documenting adult age progression. Overview of MORPH-II Released in 2008, the non-commercial version of MORPH-II contains approximately 55,134 unique facial images (primarily mugshots) of 13,000 subjects. Key characteristics include: Longitudinal Span: Images were captured between 2003 and 2007, with some individuals appearing multiple times, allowing researchers to track aging over several years. Demographic Variety: The subjects range in age from 16 to 77 years and include diverse ethnic backgrounds such as African, European, Asian, and Hispanic. Rich Metadata: Each image is accompanied by metadata for age, gender, and race, facilitating high-accuracy classification studies. The "Verified" Aspect: Cleaning and Validation While MORPH-II is a benchmark, researchers have identified that much of its raw metadata was originally self-reported , leading to inconsistencies in recorded ages or demographic data. To ensure the data is reliable for scientific use, "verified" versions or cleaning protocols have been established: Data Cleaning Whitepapers: Research teams at UNC Wilmington and other institutions have published "cleaning" strategies to correct these inconsistencies. Verification Scripts: Publicly available repositories, such as the MORPH Subgroups and Cleaning script on GitHub, provide tools to filter and verify age ranges, gender, and ethnicity before training models. Standardized Protocols: Projects like morph2-protocols offer verified "splits" (e.g., the Random, Whole, and AGR protocols) to ensure researchers can replicate and benchmark their studies using the exact same, validated data subsets. Applications in Modern Research arXiv:2007.02684v2 [cs.CV] 19 Sep 2020

Morph II Dataset — Verification-Focused Report Overview morph ii dataset verified

Name: MORPH II (often stylized MORPH-II). Type: Large longitudinal face image dataset widely used for face recognition, age estimation, and demographic studies. Size: ~55,000 images of ~13,000 subjects (male-skewed, strong age-span per subject). Common uses: age progression/regression, face verification/identification, bias and fairness research.

Verification-focused characteristics

Longitudinal pairs: multiple images per subject across time enable true positive pair construction for verification (same-subject pairs across sessions/ages). Negative pairs: plentiful distinct-subject images allow diverse impostor pairs. Controlled metadata: each image includes subject ID, birth date/age, gender, race, and capture date for temporal-split protocols and age-aware verification. MORPH-II is the second and largest release of

Recommended verification protocols

Standard same/different split (subject-disjoint train/test): ensure no subject appears in both sets. Time-based verification: form positive pairs with large age gaps to test cross-age robustness. Cross-race and cross-gender evaluation: stratify pairs to measure demographic performance differences. K-fold identity splits: repeatable identity-disjoint folds (e.g., 5-fold) for stable estimates.

Preprocessing suggestions

Face detection and alignment (consistent landmark-based affine align). Normalize resolution (e.g., 112×112 or 224×224 depending on model). Photometric normalization (per-channel mean/std or histogram matching). Remove duplicate or low-quality images; verify metadata consistency (age = capture_date − birth_date).

Evaluation metrics