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Figure 5 Template update.
The idea of template update was used by the authors in their previous study [79].
A framework is proposed that helps design engineers during the template update process by providing a decision support system and tools in order to support template instances update.
For diminishing the accumulative error in tracking, we design a new template update strategy that chooses a new template with the lowest matching error.
The effect of the template update is prominent in Region D (h), where the signal contains bigeminy, and the template (g) successfully captures the change.
As alternative, automatic template update algorithms, which exploit the own-knowledge of the mono- or multi-modal biometric system, on a batch of samples collected during system operations without the human supervision, have been proposed.
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The app checks for any template updates and downloads, validates and deploys them automatically, if necessary.
The proposed framework fills an actual gap concerning template updates, which slows down templates adoption for large-scale industrial use.
However, their solver is sensitive to a parameter termed as Lipschitz constant, which is computational load during the template updating.
The remaining two trackers IVT and L1 cannot cope with severe appearance changes, since the ground truth is not preserved in their template updating.
But for the template updating, we only apply the second criterion with a stricter threshold, i.e., ( left|right|{beta}_{G_p}Big|{Big|}_1/Big|left|beta right|{Big|}_1>0.8 ), which provides more reliable features and then prevents the template from identity drift.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com