Robust 3D Biometric Authentication via Synthetic Data Augmentation and Depth Map Refinement in Mobile Environments

This paper presents a comprehensive framework for enhancing biometric security on consumer mobile devices through the integration of advanced depth map refinement techniques and generative synthetic data augmentation. Traditional two-dimensional biometric authentication systems remain vulnerable to presentation attacks and environmental degradation, while three-dimensional approaches often suffer from hardware-imposed resolution limitations. Our methodology introduces a dual-stage architecture combining rigorous mathematical optimization for depth enhancement with conditional generative adversarial networks for dataset expansion. The depth refinement stage employs a total variation regularization model with anisotropic diffusion to recover high-fidelity surface geometry from noisy sensor inputs. The synthetic augmentation stage generates photorealistic biometric samples across diverse demographic and pathological conditions. Extensive experimental validation on a proprietary dataset of 500 subjects demonstrates significant improvements in authentication metrics. Our approach reduces the false acceptance rate by 17.3% and false rejection rate by 14.8% compared to baseline methods while maintaining real-time performance constraints on mobile hardware. The proposed framework establishes that algorithmic sophistication can effectively compensate for hardware limitations in resource-constrained environments.