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

• URL: http://arxiv.org/abs/2504.18886v1
• Date: Sat, 26 Apr 2025 10:21:46 GMT
• Title: Exploiting Multiple Representations: 3D Face Biometrics Fusion with Application to Surveillance
• Authors: Simone Maurizio La Cava, Roberto Casula, Sara Concas, Giulia Orrù, Ruben Tolosana, Martin Drahansky, Julian Fierrez, Gian Luca Marcialis,
• Abstract summary: 3D face reconstruction (3DFR) algorithms are based on specific assumptions tailored to the limits and characteristics of the different application scenarios.<n>In this study, we investigate how multiple state-of-the-art 3DFR algorithms can be used to generate a better representation of subjects.<n>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.
• Score: 6.277064632667653
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• xEdgeFace: Efficient Cross-Spectral Face Recognition for Edge Devices [4.910937238451485]
  Heterogeneous Face Recognition (HFR) addresses the challenge of matching face images across different sensing modalities.<n>We present a lightweight yet effective HFR framework by adapting a hybrid CNN-Transformer architecture.<n>Our approach enables efficient end-to-end training with minimal paired heterogeneous data while preserving strong performance on standard RGB face recognition tasks.
  arXiv  Detail & Related papers  (2025-04-28T10:03:11Z)
• OODFace: Benchmarking Robustness of Face Recognition under Common Corruptions and Appearance Variations [21.00631818827379]
  Existing open-source models and commercial algorithms lack robustness in certain complex Out-of-Distribution scenarios.<n>We introduce OODFace, which explores the OOD challenges faced by facial recognition models from two perspectives.<n>We offer a unified toolkit that includes all corruption and variation types, easily extendable to other datasets.
  arXiv  Detail & Related papers  (2024-12-03T14:42:31Z)
• ID$^3$: Identity-Preserving-yet-Diversified Diffusion Models for Synthetic Face Recognition [60.15830516741776]
  Synthetic face recognition (SFR) aims to generate datasets that mimic the distribution of real face data. We introduce a diffusion-fueled SFR model termed $textID3$. $textID3$ employs an ID-preserving loss to generate diverse yet identity-consistent facial appearances.
  arXiv  Detail & Related papers  (2024-09-26T06:46:40Z)
• Exploring 3D Face Reconstruction and Fusion Methods for Face Verification: A Case-Study in Video Surveillance [6.277064632667653]
  3D face reconstruction (3DFR) algorithms are based on specific assumptions tailored to distinct application scenarios. We show that the complementarity induced by different 3DFR algorithms improves performance when tests are conducted at never-seen-before distances from the camera.
  arXiv  Detail & Related papers  (2024-09-16T17:17:47Z)
• Confidence-Aware RGB-D Face Recognition via Virtual Depth Synthesis [48.59382455101753]
  2D face recognition encounters challenges in unconstrained environments due to varying illumination, occlusion, and pose. Recent studies focus on RGB-D face recognition to improve robustness by incorporating depth information. In this work, we first construct a diverse depth dataset generated by 3D Morphable Models for depth model pre-training. Then, we propose a domain-independent pre-training framework that utilizes readily available pre-trained RGB and depth models to separately perform face recognition without needing additional paired data for retraining.
  arXiv  Detail & Related papers  (2024-03-11T09:12:24Z)
• Towards Unified 3D Object Detection via Algorithm and Data Unification [70.27631528933482]
  We build the first unified multi-modal 3D object detection benchmark MM- Omni3D and extend the aforementioned monocular detector to its multi-modal version. We name the designed monocular and multi-modal detectors as UniMODE and MM-UniMODE, respectively.
  arXiv  Detail & Related papers  (2024-02-28T18:59:31Z)
• COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection [56.7599217711363]
  Face forgery recognition methods can only process one face at a time. Most face forgery recognition methods can only process one face at a time. We propose COMICS, an end-to-end framework for multi-face forgery detection.
  arXiv  Detail & Related papers  (2023-08-03T03:37:13Z)
• Advancing 3D finger knuckle recognition via deep feature learning [51.871256510747465]
  Contactless 3D finger knuckle patterns have emerged as an effective biometric identifier due to its discriminativeness, visibility from a distance, and convenience. Recent research has developed a deep feature collaboration network which simultaneously incorporates intermediate features from deep neural networks with multiple scales. This paper advances this approach by investigating the possibility of learning a discriminative feature vector with the least possible dimension for representing 3D finger knuckle images.
  arXiv  Detail & Related papers  (2023-01-07T20:55:16Z)
• Hierarchical Deep CNN Feature Set-Based Representation Learning for Robust Cross-Resolution Face Recognition [59.29808528182607]
  Cross-resolution face recognition (CRFR) is important in intelligent surveillance and biometric forensics. Existing shallow learning-based and deep learning-based methods focus on mapping the HR-LR face pairs into a joint feature space. In this study, we desire to fully exploit the multi-level deep convolutional neural network (CNN) feature set for robust CRFR.
  arXiv  Detail & Related papers  (2021-03-25T14:03:42Z)
• Face recognition using PCA integrated with Delaunay triangulation [0.0]
  The research examines the integration of Principal Component Analysis with Delaunay Triangulation. The method triangulates a set of face landmark points and obtains eigenfaces of the provided images. It compares the algorithm with traditional PCA and discusses the inclusion of different face landmark points to deliver an effective recognition rate.
  arXiv  Detail & Related papers  (2020-11-25T14:46:08Z)
• Generalized Iris Presentation Attack Detection Algorithm under Cross-Database Settings [63.90855798947425]
  Presentation attacks pose major challenges to most of the biometric modalities. We propose a generalized deep learning-based presentation attack detection network, MVANet. It is inspired by the simplicity and success of hybrid algorithm or fusion of multiple detection networks.
  arXiv  Detail & Related papers  (2020-10-25T22:42:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.