SECURE CRYPTO-BIOMETRIC SYSTEM FOR CLOUD COMPUTING

Cloud computing has achieved maturity, and there is a heterogeneous group of providers and cloud-based services. However, significant attention remains focused on security concerns. In many cases, security and privacy issues are a significant barrier to user acceptance of cloud computing systems and the advantages these offer with respect to previous systems. Biometric technologies are becoming the key aspect of a wide range of secure identification and personal verification solutions, but in a cloud computing environment they present some problems related to the management of biometric data, due to privacy regulations and the need to trust cloud providers. To overcome those problems in this paper, we propose a cryptobiometric system applied to cloud computing in which no private biometric data are exposed

A Gaussian Process Model for Color Camera Characterization: Assessment in Outdoor Levantine Rock Art Scenes

[EN] In this paper, we propose a novel approach to undertake the colorimetric camera characterization procedure based on a Gaussian process (GP). GPs are powerful and flexible nonparametric models for multivariate nonlinear functions. To validate the GP model, we compare the results achieved with a second-order polynomial model, which is the most widely used regression model for characterization purposes. We applied the methodology on a set of raw images of rock art scenes collected with two different Single Lens Reflex (SLR) cameras. A leave-one-out cross-validation (LOOCV) procedure was used to assess the predictive performance of the models in terms of CIE XYZ residuals and Delta E-ab* color differences. Values of less than 3 CIELAB units were achieved for Delta E-ab*. The output sRGB characterized images show that both regression models are suitable for practical applications in cultural heritage documentation. However, the results show that colorimetric characterization based on the Gaussian process provides significantly better results, with lower values for residuals and Delta E-ab*. We also analyzed the induced noise into the output image after applying the camera characterization. As the noise depends on the specific camera, proper camera selection is essential for the photogrammetric work.This research is partly funded by the Research and Development Aid Program PAID-01-16 of the Universitat Politecnica de Valencia, through FPI-UPV-2016 Sub 1 grant.Molada-Tebar, A.; Riutort-Mayol, G.; Marqués-Mateu, Á.; Lerma, JL. (2019). A Gaussian Process Model for Color Camera Characterization: Assessment in Outdoor Levantine Rock Art Scenes. Sensors. 19(21):1-22. https://doi.org/10.3390/s19214610S1221921Ruiz, J. F., & Pereira, J. (2014). The colours of rock art. Analysis of colour recording and communication systems in rock art research. 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