Problem
Research questions and friction points this paper is trying to address.
face recognition
synthetic data
controllable face synthesis
generalization
data diversity
Adaptive Adversarial Augmentation for Controllable Face Synthesis
🤖 AI Summary
This study addresses the limited diversity of synthetic data in face recognition and its difficulty in generalizing to complex scenarios involving low resolution and occlusion. To overcome these limitations, this work proposes an Ensemble Feedback Controllable Synthesis (EFCS) framework that integrates adversarial augmentation with distribution evaluation metrics to guide image generation. Furthermore, it introduces an analysis-driven formulation that quantifies the relationships among perturbation difficulty, sample utility, and performance degradation, thereby providing theoretical support for complexity balancing. The proposed approach effectively expands distributional variability while preserving visual realism. Extensive experiments demonstrate that the generative model significantly outperforms baselines across multiple benchmarks, successfully bridging the generalization gap between synthetic and real-world data.
📝 Abstract
Synthetic data provides a scalable alternative to real-world datasets for training face recognition models, particularly under challenging conditions such as low resolution, occlusion, and masks. Yet, most approaches lack diversity and fail to generalize effectively. We propose Ensemble Feedback Controllable Synthesis (EFCS), a guided framework that generates diverse and challenging samples while preserving visual realism. EFCS expands distributional variability, often reflected in higher FID and KID scores compared to single-feedback and random synthesis, while maintaining high precision. Recognition models trained on EFCS data consistently outperform baselines across multiple benchmarks, showing improved generalization to real-world scenarios. Furthermore, we introduce an analytically motivated formulation linking perturbation-induced difficulty, sample utility, and performance degradation, offering principled insights into balancing synthetic data complexity for optimal training. Together, these contributions establish EFCS as an effective and analytically grounded approach for bridging the gap between synthetic and real datasets.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Controllable Face Synthesis
Adaptive Adversarial Augmentation
Ensemble Feedback
Synthetic Data Generalization
Perturbation Analysis
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