EigenFace-256 Dataset Construction

Training a robust face embedding model requires a diverse dataset with multiple images per identity—capturing variations in angles, lighting, expressions, and age. Using real-world data for this purpose poses several challenges:

Privacy and Ethics: Real-world images can lead to legal and ethical complications.
Bias and Imbalance: Datasets based on real images may lack diversity.
Data Labeling Complexity: Annotating large datasets is time-consuming and costly.

Synthetic Approach

To overcome these issues, we built EigenFace-256, a fully synthetic dataset designed to maintain consistent identity across controlled variations. Key features include:

Controlled Variations: Multiple images per identity with different angles, lighting, and expressions.
Age Diversity: Variations that allow models to generalize across different life stages.
Ethically Sound: 100% synthetic images, eliminating privacy concerns.
Balanced Data: Diverse training samples to reduce demographic bias.

Unlike many synthetic datasets that generate single-image identities, EigenFace-256 provides multiple controlled variations per identity, contributing to more robust embeddings.