Título:
An a-contrario biometric fusion approach.
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Autor:
Di Martino, Luis
Preciozzi, Javier
Grompone von Gioi, Rafael
Garella, Guillermo
Fernández, Alicia
Lecumberry, Federico
Preciozzi, Javier
Grompone von Gioi, Rafael
Garella, Guillermo
Fernández, Alicia
Lecumberry, Federico
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Artículo
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BIOMETRIA
Año de publicación:
2020
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Resumen:
Fusion is a key component in many biometric systems: it is one of the most widely used techniques to improve their accuracy. Each time we need to combine the output of systems that use different biometric traits, or different samples of the same biometric trait, or even different algorithms, we need to define a fusion strategy. Independently of the fusion method used, there is always a decision step, in which it is decided if the traits being compared correspond to the same individual or not. In this work, we present a statistical decision criterion based on the a-contrario framework, which has already proven to be useful in biometric applications. The proposed method and its theoretical background is described in detail, and its application to biometric fusion is illustrated with simulated and real data.
Descripción:
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IEEE-CVF
EN:
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 14-19 Jun, pp. 822--823.
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Citación:
Di Martino, L., Preciozzi, J., Grompone von Gioi, R., y otros. An a-contrario biometric fusion approach. En : 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops [en línea]. [s.l.] : IEEE-CVF, 2020.
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Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| DPGGFL20.pdf | Conferencia | 861.68 KB | Adobe PDF |