Once confined to indoor biometric applications depending on dedicated acquisition devices, recently the iris has proved to be a suitable biometric for in-the-wild ubiquitous person authentication, thanks to continuously improving image capturing/processing performances provided by last generations of smartphones. In this mobile context, the efficiency of the whole processing pipeline represents a crucial aspect of any practical application and the segmentation task, that is deeply affected by noisy iris images may become a serious bottleneck. This work presents F-FID, an effective and time-wise efficient approach to de-noising of iris images by means of a fuzzy controller without sacrificing their resolution and saliency. The experiments, specifically conducted on the MICHE dataset, confirm that the proposed method provides segmentation accuracy comparable to that achieved by state of the art algorithms, while requiring less than twenty percent of their average computing time.

F-FID: fast fuzzy-based iris de-noising for mobile security applications

Ricciardi S.
2019

Abstract

Once confined to indoor biometric applications depending on dedicated acquisition devices, recently the iris has proved to be a suitable biometric for in-the-wild ubiquitous person authentication, thanks to continuously improving image capturing/processing performances provided by last generations of smartphones. In this mobile context, the efficiency of the whole processing pipeline represents a crucial aspect of any practical application and the segmentation task, that is deeply affected by noisy iris images may become a serious bottleneck. This work presents F-FID, an effective and time-wise efficient approach to de-noising of iris images by means of a fuzzy controller without sacrificing their resolution and saliency. The experiments, specifically conducted on the MICHE dataset, confirm that the proposed method provides segmentation accuracy comparable to that achieved by state of the art algorithms, while requiring less than twenty percent of their average computing time.
https://link.springer.com/journal/11042
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11695/86740
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