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We describe a template-based framework to bind class-specific information to a set of image patterns and retrieve that information by matching the template to a query pattern of the same class. This is done by mapping the class-specific information to a set of spatial translations which are applied to the set of image patterns from which a template is designed taking advantage of the properties of correlation filters. The bound information is retrieved during matching with an authentic query by estimating the spatial translations applied to the images that were used to design the template. In this paper we focus on the problem of binding information to biometric signatures as an application of our framework. Our framework is flexible enough to allow spreading the information to be bound over multiple pattern classes which in the context of biometric key-binding enables multi-class and multi-modal biometric key-binding. We demonstrate the effectiveness of the proposed scheme via extensive numerical results on multiple biometric databases.


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Adversary can override the output of conventional biometric matching algorithms.

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Biometric Key-Binding monolithically binds and retrieves cryptographic keys to biometric templates thereby combining the matching and key-release into a single step.

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Classifier output is the \(\textbf{key}\) itself instead of a simple yes/no binary output.


  • Multi-Peak correlation filter design for binding cryptographic keys to biometric signatures.
  • Proposed a probabilistic decoding scheme for biometric key retrieval.
  • Proposed extensions to multi-user and multi-modal biometric key-binding.