🤖 AI Summary
This study addresses the dual challenge in face verification systems: defending against presentation attacks (e.g., photos, videos) while maintaining robustness to legitimate appearance variations such as accessories, lighting, and pose changes. The authors propose a unified framework that, for the first time, integrates classical handcrafted features—including PCA, LBP, HOG, SURF, and Harris corner detectors—within five fusion strategies: Product Matching (PM), Linear Product Matching (LPM), Hierarchical Product Matching (HPM), Sum Matching (SM), and Hybrid Matching (HM). These strategies jointly optimize spoof detection and recognition robustness across preprocessing and classification stages. Experimental results demonstrate that HPM achieves 94.59% accuracy under mixed spoofing attacks, 81.5%–93.2% under lighting and pose variations, and 91.67% anti-spoofing performance; LPM yields the best anti-spoofing rate (93.2%) but exhibits weaker pose robustness. The work further reveals a quantifiable sensitivity–robustness trade-off between spoof detection and appearance invariance.
📝 Abstract
Face recognition systems face two distinct, commonly-separated failure modes: spoofing, where an impostor presents a photograph or video of an authorized user, and disguise, where a legitimate user is rejected because their appearance differs from their enrolled template due to accessories, facial hair, illumination, or pose. This paper proposes and compares five combined feature-extraction and classification pipelines that address both problems within a single framework: PM (PCA and Minimum Euclidean Distance, MED), LPM (Local Binary Patterns with PCA and MED), HPM (Histogram of Oriented Gradients with PCA and MED), SM (Speeded-Up Robust Features with MED), and HM (Harris corner features with MED). Each pipeline follows a common two-phase process comprising pre-processing, feature extraction, feature filtering, and classification. The methods were trained on 115 subjects drawn from the FEI, Disguised Faces Database, and NUAA databases and evaluated on six test conditions covering mixed appearances, frontal faces, dark illumination, left- and right-turned poses, and photo-spoofing attempts. The HOG-based pipeline (HPM) achieved the most consistent performance across conditions, with 94.59% accuracy on mixed-appearance disguise, 81.5-93.2% across pose and illumination variants, and 91.67% on spoofing, while the LBP-based pipeline (LPM) achieved the highest spoofing-detection accuracy (93.2%) but weaker robustness to pose change. These results reveal a measurable trade-off between spoof sensitivity and disguise robustness among classical feature representations, motivating the deep-learning and cross-database extensions discussed in the concluding sections.