mlsanityData to 7 Oct 2026

A Comprehensive Overview of Biometric Fusion

arXiv 1902.02919 ↗First posted 8 Feb 2019cs.CVcited by 18

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Abstract (arXiv)

The performance of a biometric system that relies on a single biometric modality (e.g., fingerprints only) is often stymied by various factors such as poor data quality or limited scalability. Multibiometric systems utilize the principle of fusion to combine information from multiple sources in order to improve recognition accuracy whilst addressing some of the limitations of single-biometric systems. The past two decades have witnessed the development of a large number of biometric fusion schemes. This paper presents an overview of biometric fusion with specific focus on three questions: what to fuse, when to fuse, and how to fuse. A comprehensive review of techniques incorporating ancillary information in the biometric recognition pipeline is also presented. In this regard, the following topics are discussed: (i) incorporating data quality in the biometric recognition pipeline; (ii) combining soft biometric attributes with primary biometric identifiers; (iii) utilizing contextual information to improve biometric recognition accuracy; and (iv) performing continuous authentication using ancillary information. In addition, the use of information fusion principles for presentation attack detection and multibiometric cryptosystems is also discussed. Finally, some of the research challenges in biometric fusion are enumerated. The purpose of this article is to provide readers a comprehensive overview of the role of information fusion in biometrics.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Feb 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 2; 2023: 3; 2024: 3; 2025: 3; 2026: 1.

18 papers.

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  1. 2021A Comprehensive Study on Face Recognition Biases Beyond Demographicscited by 86
  2. 202550 Years of Automated Face Recognitioncited by 13
  3. 2025A Quality-Guided Mixture of Score-Fusion Experts Framework for Human Recognitioncited by 11
  4. 2021Depth as Attention for Face Representation Learningcited by 5
  5. 2021Feature Fusion Methods for Indexing and Retrieval of Biometric Data: Application to Face Recognition with Privacy Protectioncited by 5
  6. 2021Hand-Based Person Identification using Global and Part-Aware Deep Feature Representation Learningcited by 4
  7. 2022PIC-Score: Probabilistic Interpretable Comparison Score for Optimal Matching Confidence in Single- and Multi-Biometric (Face) Recognitioncited by 4
  8. 2023Joint Person Identity, Gender and Age Estimation from Hand Images using Deep Multi-Task Representation Learningcited by 4
  9. 2022Multimodal Learning for Multi-Omics: A Surveycited by 3
  10. 2026FusionAgent: A Multimodal Agent with Dynamic Model Selection for Human Recognitioncited by 3
  11. 2021Quality-Aware Multimodal Biometric Recognitioncited by 2
  12. 2023Privacy-preserving Multi-biometric Indexing based on Frequent Binary Patternscited by 2
6 more
  1. 2021Signal-level Fusion for Indexing and Retrieval of Facial Biometric Datacited by 1
  2. 2023A survey on deep learning approaches for data integration in autonomous driving systemcited by 1
  3. 2024Detecting Face Synthesis Using a Concealed Fusion Modelcited by 0
  4. 2024Fusion of Minutia Cylinder Codes and Minutia Patch Embeddings for Latent Fingerprint Recognitioncited by 0
  5. 2024On Missing Scores in Evolving Multibiometric Systemscited by 0
  6. 2025Exploring Complementarity and Explainability in CNNs for Periocular Verification Across Acquisition Distancescited by 0