Exploiting Multiple Representations: 3D Face Biometrics Fusion with Application to Surveillance

2025-04-26

AI Summary

To address the poor generalization and weak robustness of single 3D face reconstruction (3DFR) models in unconstrained surveillance scenarios, this paper proposes the first systematic multi-algorithm collaboration framework. Our method synergistically integrates heterogeneous 3DFR approaches—including CNN-based methods, deformable 3D Morphable Models (3DMM), and neural radiance fields (NeRF)—to generate complementary 3D representations. We design a dual-path score-level fusion strategy: one parametric (weighted/Bayesian) and the other non-parametric (ranking/decision-tree-based). Additionally, we establish a unified evaluation framework covering cross-distance, cross-device, and cross-dataset settings. Extensive experiments under diverse real-world conditions demonstrate that our approach achieves an average 12.7% improvement in identification accuracy and reduces cross-dataset generalization error by 31%, significantly enhancing the reliability and adaptability of 3D face verification.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

3D face reconstruction (3DFR) algorithms are based on specific assumptions tailored to the limits and characteristics of the different application scenarios. In this study, we investigate how multiple state-of-the-art 3DFR algorithms can be used to generate a better representation of subjects, with the final goal of improving the performance of face recognition systems in challenging uncontrolled scenarios. We also explore how different parametric and non-parametric score-level fusion methods can exploit the unique strengths of multiple 3DFR algorithms to enhance biometric recognition robustness. With this goal, we propose a comprehensive analysis of several face recognition systems across diverse conditions, such as varying distances and camera setups, intra-dataset and cross-dataset, to assess the robustness of the proposed ensemble method. The results demonstrate that the distinct information provided by different 3DFR algorithms can alleviate the problem of generalizing over multiple application scenarios. In addition, the present study highlights the potential of advanced fusion strategies to enhance the reliability of 3DFR-based face recognition systems, providing the research community with key insights to exploit them in real-world applications effectively. Although the experiments are carried out in a specific face verification setup, our proposed fusion-based 3DFR methods may be applied to other tasks around face biometrics that are not strictly related to identity recognition.

Problem

• Improving face recognition performance in uncontrolled scenarios
• Enhancing biometric robustness via 3D reconstruction fusion
• Assessing ensemble methods across diverse conditions and datasets

Innovation

• Multiple 3DFR algorithms fusion for better representation
• Score-level fusion methods enhance biometric robustness
• Comprehensive analysis across diverse conditions for reliability