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

• Published in: Proceedings (IEEE Winter Conference on Applications of Computer Vision Workshops. Online) pp. 1322 - 1330
• Main Authors: Tandon, Abhishek, Sharma, Geetanjali, Jaswal, Gaurav, Nigam, Aditya, Ramachandra, Raghavendra
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.02.2025
• Subjects: b-spline curves; biometrics; Data models; diffusion models; Forehead; forehead-creases images; Image edge detection; Polynomials; Protocols; Splines (mathematics); Synthetic data; synthetic data generation; Training data; Translation; Visualization
• ISSN: 2690-621X
• DOI: 10.1109/WACVW65960.2025.00155

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.