Generating Realistic Forehead-Creases for User Verification via Conditioned Piecewise Polynomial Curves

Abhishek Tandon, Geetanjali Sharma, Gaurav Jaswal, Aditya Nigam, Raghavendra Ramachandra; Proceedings of the Winter Conference on Applications of Computer Vision (WACV) Workshops, 2025, pp. 1412-1420

We propose a trait-specific image generation method that models forehead creases geometrically using B-spline and Bezier 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.