In this contribution, we propose an adaptive multiresolution denoising technique operating in the wavelet domain that
selectively enhances object contours, extending a restoration scheme based on edge oriented wavelet representation by
means of adaptive surround inhibition inspired by the human visual system characteristics. The use of the complex edge
oriented wavelet representation is motivated by the fact that it is tuned to the most relevant visual image features. In this
domain, an edge is represented by a complex number whose magnitude is proportional to its "strength" while phase
equals the orientation angle. The complex edge wavelet is the first order dyadic Laguerre Gauss Circular Harmonic
Wavelet, acting as a band limited gradient operator. The anisotropic sharpening function enhances or attenuates
large/small edges more or less deeply, accounting for masking effects induced by textured background. Adapting
sharpening to the local image content is realized by identifying the local statistics of natural and artificial textures like
grass, foliage, water, composing the background. In the paper, the whole mathematical model is derived and its
performances are validated on the basis of simulations on a wide data set.
Biometrics is the most emerging technology for automatic people authentication, nevertheless severe concerns
raised about security of such systems and users' privacy. In case of malicious attacks toward one or more components
of the authentication system, stolen biometric features cannot be replaced. This paper focuses on securing
the enrollment database and the communication channel between such database and the matcher. In particular,
a method is developed to protect the stored biometric templates, adapting the fuzzy commitment scheme to iris
biometrics by exploiting error correction codes tailored on template discriminability. The aforementioned method
allows template renewability applied to iris based authentication and guarantees high security performing the
match in the encrypted domain.
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