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

2025-01-23

AI Summary

This study addresses the challenges of limited sample availability and difficulty in modeling inter-individual variability in forehead wrinkle biometrics. Methodologically, it introduces a novel paradigm integrating geometric modeling and generative learning for wrinkle synthesis and verification. Specifically, it pioneers the use of B-spline and Bézier curve parameterization to characterize forehead wrinkle structures, enabling high-fidelity generation of primary and secondary wrinkle patterns; an edge-to-image diffusion model, guided by geometric visual cues, achieves controllable edge-to-texture synthesis. To ensure both diversity and identity consistency, constrained control-point perturbation and wrinkle-customized augmentation are incorporated. Under a cross-dataset evaluation protocol, joint training with synthetic and real data significantly improves verification accuracy (+5.2%), while enhancing model generalizability and robustness against domain shift and occlusion.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Learning on the Edge & Model Compression
• Natural Language Processing: Generation

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Web data generation and simulation
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• Individual forehead wrinkle recognition
• Accuracy improvement
• Identity confirmation

Innovation

• Forehead Wrinkle Simulation
• Special Curve Adjustment
• Enhanced Recognition Accuracy