Open AccessPosted Content
A Comprehensive Overview of Biometric Fusion
TLDR
The purpose of this article is to provide readers a comprehensive overview of the role of information fusion in biometrics with specific focus on three questions: what to fusion, when to fuse, and how to fuse.Abstract:
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.read more
Citations
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DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection
TL;DR: This survey provides a thorough review of techniques for manipulating face images including DeepFake methods, and methods to detect such manipulations, with special attention to the latest generation of DeepFakes.
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Face Image Quality Assessment: A Literature Survey
Torsten Schlett,Christian Rathgeb,Olaf Henniger,Javier Galbally,Julian Fierrez,Christoph Busch +5 more
TL;DR: This survey provides an overview of the face image quality assessment literature, which predominantly focuses on visible wavelength face image input and a trend towards deep learning based methods is observed, including notable conceptual differences among the recent approaches.
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Biometric Quality: Review and Application to Face Recognition with FaceQnet
TL;DR: FaceQnet is a novel opensource face quality assessment tool, inspired and powered by deep learning technology, which assigns a scalar quality measure to facial images, as prediction of their recognition accuracy.
Proceedings ArticleDOI
DroneSURF: Benchmark Dataset for Drone-based Face Recognition
TL;DR: This research presents a novel large-scale drone dataset, DroneSURF: Drone Surveillance of Faces, in order to facilitate research for face recognition, along with information regarding the data distribution, protocols for evaluation, and baseline results.
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Computational workload in biometric identification systems: an overview
TL;DR: Taxonomy for conceptual categorisation of methods with the aim of reducing or optimising the computational workload, and thereby speeding-up the identification transactions, in biometric identification systems are presented.
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Book
Handbook of Fingerprint Recognition
TL;DR: This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators.