Multi-Biometrics: Survey and Projection of a New Biometric System
Abdoul Kamal Assouma1, Tahirou Djara2, Abdou-Aziz Sobabe3
1Abdoul Kamal Assouma, Department of Computer Engineering and Telecommunications, Polytechnic School of Abomey-Calavi/ University of Abomey-Calavi, Abomey-Calavi, Benin.
2Tahirou Djara, Department of Computer Engineering and Telecommunications, Polytechnic School of Abomey-Calavi/ University of Abomey-Calavi, Abomey-Calavi, Benin.
3Abdou-Aziz Sobabe, Department of Computer Engineering and Telecommunications, Polytechnic School of Abomey-Calavi/ University of Abomey-Calavi, Abomey-Calavi, Benin.
Manuscript received on 16 January 2023 | Revised Manuscript received on 10 February 2023 | Manuscript Accepted on 15 February 2023 | Manuscript published on 28 February 2023 | PP: 80-87 | Volume-12 Issue-3, February 2023 | Retrieval Number: 100.1/ijeat.C40080212323 | DOI: 10.35940/ijeat.C4008.0212323
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Multi-biometric systems using feature-level fusion allow more accuracy and reliability in recognition performance than uni-biometric systems. But in practice, this type of fusion is difficult to implement especially when we are facing heterogeneous biometric modalities or incompatible features. The major challenge of feature fusion is to produce a representation of each modality with an excellent level of discrimination. Beyond pure biometric modalities, the use of metadata has proven to improve the performance of biometric systems. In view of these findings, our work focuses on multi-origin biometrics which allows the use of pure biometric modalities and metadata in a feature fusion strategy. The main objective of this paper is to present an overview of biometrics as bordered in the literature with a particular focus on multibiometrics and to propose a model of a multi-origin biometric system using pure biometric and soft biometric modalities in a feature-level fusion strategy. The curvelet transformation and the order statistics are proposed respectively for the extraction the feature of the pure biometric modalities, and for the selection of the relevant feature of each modality in order to ensure a good level of discrimination of the individuals. In this paper, we have presented the overview of biometrics through its concepts, modalities, advantages, disadvantages and implementation architectures. A focus has been put on multi-biometrics with the presentation of a harmonized process for feature fusion. For the experiments, we proposed a global model for feature fusion in a multi-origin system using face and iris modalities as pure biometrics, and facial skin color as metadata. This system and the results will be presented in future work.
Keywords: Features, Fusion, Hierarchical-Sequential, Multi-Origin.
Scope of the Article: Software Dependability, Reliability, Scalability