1. Benchmarking Quality-Dependent and Cost-Sensitive Score-Level Multimodal Biometric Fusion Algorithms
- Author
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Bernadette Dorizzi, Fernando Alonso-Fernandez, L. Allano, Albert Ali Salah, Tobias Scheidat, Norman Poh, Javier Ortega-Garcia, Donald E. Maurer, Julian Fierrez, John P. Baker, O. Fatukasi, Harald Ganster, Thirimachos Bourlai, Josef Kittler, Claus Vielhauer, Onkar Ambekar, UAM. Departamento de Ingeniería Informática, Análisis y Tratamiento de Voz y Señales Biométricas (ING EPS-002), Centre for Vision, Speech and Signal Processing (CVSSP), University of Surrey (UNIS), Lane Department of Computer Science and Electrical Engineering [Virginia] (LCSEE), West Virginia University [Morgantown], Département Electronique et Physique (EPH), Institut Mines-Télécom [Paris] (IMT)-Télécom SudParis (TSP), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Biometric Recognition Group (Universidad Autonoma de Madrid) (ATVS), Centrum Wiskunde & Informatica (CWI), Johns Hopkins University Applied Physics Laboratory [Laurel, MD] (APL), Institute of Digital Image Processing (Joanneum Research) (DIP), Intelligent Systems Lab Amsterdam - Intelligent Sensory Information Systems (University of Amsterdam) (ISLA-ISIS), Department of Informatics and Media (University of Applied Sciences Brandenburg), Institut für Technische und Betriebliche Informationssysteme (Otto-von-Guericke-Universität Magdeburg) (ITI), Signals and Images, and Département Electronique et Physique (TSP - EPH)
- Subjects
FOS: Computer and information sciences ,Computer Science - Cryptography and Security ,Biometrics ,Computer Networks and Communications ,Computer science ,Computer Vision and Pattern Recognition (cs.CV) ,Data_MISCELLANEOUS ,Computer Science - Computer Vision and Pattern Recognition ,Access control ,Signalbehandling ,02 engineering and technology ,[INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR] ,Software ,Cost-sensitive fusion ,0202 electrical engineering, electronic engineering, information engineering ,Biometric database ,Safety, Risk, Reliability and Quality ,Quality-based fusion ,Informática ,Authentication ,business.industry ,Fingerprint (computing) ,Multimodal biometric authentication ,020206 networking & telecommunications ,Benchmarking ,Security controls ,Signal Processing ,Benchmark (computing) ,020201 artificial intelligence & image processing ,business ,Algorithm ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing ,Cryptography and Security (cs.CR) - Abstract
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works., Automatically verifying the identity of a person by means of biometrics (e.g., face and fingerprint) is an important application in our day-to-day activities such as accessing banking services and security control in airports. To increase the system reliability, several biometric devices are often used. Such a combined system is known as a multimodal biometric system. This paper reports a benchmarking study carried out within the framework of the BioSecure DS2 (Access Control) evaluation campaign organized by the University of Surrey, involving face, fingerprint, and iris biometrics for person authentication, targeting the application of physical access control in a medium-size establishment with some 500 persons. While multimodal biometrics is a well-investigated subject in the literature, there exists no benchmark for a fusion algorithm comparison. Working towards this goal, we designed two sets of experiments: quality-dependent and cost-sensitive evaluation. The quality-dependent evaluation aims at assessing how well fusion algorithms can perform under changing quality of raw biometric images principally due to change of devices. The cost-sensitive evaluation, on the other hand, investigates how well a fusion algorithm can perform given restricted computation and in the presence of software and hardware failures, resulting in errors such as failure-to-acquire and failure-to-match. Since multiple capturing devices are available, a fusion algorithm should be able to handle this nonideal but nevertheless realistic scenario. In both evaluations, each fusion algorithm is provided with scores from each biometric comparison subsystem as well as the quality measures of both the template and the query data. The response to the call of the evaluation campaign proved very encouraging, with the submission of 22 fusion systems. To the best of our knowledge, this campaign is the first attempt to benchmark quality-based multimodal fusion algorithms. In the presence of changing - - image quality which may be due to a change of acquisition devices and/or device capturing configurations, we observe that the top performing fusion algorithms are those that exploit automatically derived quality measurements. Our evaluation also suggests that while using all the available biometric sensors can definitely increase the fusion performance, this comes at the expense of increased cost in terms of acquisition time, computation time, the physical cost of hardware, and its maintenance cost. As demonstrated in our experiments, a promising solution which minimizes the composite cost is sequential fusion, where a fusion algorithm sequentially uses match scores until a desired confidence is reached, or until all the match scores are exhausted, before outputting the final combined score., This evaluation was made possible thanks to the EU funding from the following projects: BioSecure (www.biosecure.info) and Mobio (www.mobioproject.org). The participating teams are supported by their respective national fund bodies: the Dutch BSIK/BRICKS project and the Spanish project TEC2006-13141-C03-03.
- Published
- 2021
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