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

arXiv: https://arxiv.org/abs/2501.13889 · HTML: https://mlsanity.com/paper/2501.13889/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 26 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [AdaFace: Quality Adaptive Margin for Face Recognition](https://mlsanity.com/paper/2204.00964/) (2022)
- [BBDM: Image-to-image Translation with Brownian Bridge Diffusion Models](https://mlsanity.com/paper/2205.07680/) (2022)
- [Conditional Image Generation with Score-Based Diffusion Models](https://mlsanity.com/paper/2111.13606/) (2021)
- [RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition](https://mlsanity.com/paper/2307.14016/) (2023)
- [FH-SSTNet: Forehead Creases based User Verification using Spatio-Spatial Temporal Network](https://mlsanity.com/paper/2403.16202/) (2024)
- [GenPalm: Contactless Palmprint Generation with Diffusion Models](https://mlsanity.com/paper/2406.00287/) (2024)
- [Synthetic Forehead-creases Biometric Generation for Reliable User Verification](https://mlsanity.com/paper/2408.15693/) (2024)

## Abstract (arXiv)

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.