Generating Realistic Forehead-Creases for User Verification via Conditioned Piecewise Polynomial Curves
This work addresses the need for enhanced biometric verification systems, specifically for forehead-crease analysis, by providing a method to generate synthetic data, though it is incremental as it builds on existing geometric modeling and diffusion techniques.
The paper tackled the problem of generating realistic forehead-crease images for user verification by modeling creases geometrically with B-spline and Bézier curves and using a diffusion-based model to create synthetic identities, which improved verification system performance under a cross-database protocol.
We propose a trait-specific image generation method that models forehead creases geometrically using B-spline and Bézier curves. This approach ensures the realistic generation of both principal creases and non-prominent crease patterns, effectively constructing detailed and authentic forehead-crease images. These geometrically rendered images serve as visual prompts for a diffusion-based Edge-to-Image translation model, which generates corresponding mated samples. The resulting novel synthetic identities are then used to train a forehead-crease verification network. To enhance intra-subject diversity in the generated samples, we employ two strategies: (a) perturbing the control points of B-splines under defined constraints to maintain label consistency, and (b) applying image-level augmentations to the geometric visual prompts, such as dropout and elastic transformations, specifically tailored to crease patterns. By integrating the proposed synthetic dataset with real-world data, our method significantly improves the performance of forehead-crease verification systems under a cross-database verification protocol.