| |
Current facial recognition systems face critical limitations in counter-intelligence applications due to non-cooperative subjects and degraded acquisition conditions. This research addresses the fundamental scientific problem of quantifying and overcoming the performance degradation that occurs when facial recognition systems encounter deliberate evasion techniques combined with challenging environmental factors. We developed a novel three-component architecture combining Conditional Feature Extraction (CFE), Evasion Detection Module (EDM), and Context-Aware Transfer Learning (CATL). Experimentation utilized our SecureFace dataset (12,900 images) with performance evaluation across 17 evasion techniques and 9 environmental variations, measured using standard and operationally relevant metrics. Our approach achieved 89.4% accuracy on field-collected data compared to 51.7-72.3% for state-of-the-art methods, demonstrating 42.7% improvement against evasion techniques. The system maintained real-time performance (21.3 FPS) while achieving 75.8% accuracy at medium range (8m), compared to 58.7% for the best baseline method. The research provides novel theoretical and practical contributions: (1) a formalization of facial recognition under adversarial conditions, (2) context-aware adaptation mechanisms proven effective in real-world scenarios, and (3) implementation techniques suitable for deployment in operational settings. Performance improvements were most significant in medium-range scenarios (3-8m) and against physical evasion techniques, addressing critical gaps in current systems.
Keywords:facial analysis, adverse conditions, evasion detection, transfer learning, counterintelligence, recognition in uncontrolled environments, context-aware learning, adaptive feature extraction, security applications, robustness evaluation.
|
LEGAL
INFORMATION:
