We propose a trait-specific image generation method that models forehead creases geometrically using B-spline and B\'ezier 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.
@article{arxiv.2501.13889,
title = {Generating Realistic Forehead-Creases for User Verification via Conditioned Piecewise Polynomial Curves},
author = {Abhishek Tandon and Geetanjali Sharma and Gaurav Jaswal and Aditya Nigam and Raghavendra Ramachandra},
journal= {arXiv preprint arXiv:2501.13889},
year = {2025}
}