Some Information Geometric Aspects of Cyber Security by Face Recognition

Dodson, CTJ and Soldera, John and Scharcanski, J (2021) Some Information Geometric Aspects of Cyber Security by Face Recognition. Entropy, 23 (878). pp. 1-10.

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Abstract

Secure user access to devices and datasets is widely enabled by fingerprint or face recognition. Organization of the necessarily large secure digital object datasets, with objects having content that may consist of images, text, video or audio, involves efficient classification and feature retrieval processing. This usually will require multidimensional methods applicable to data that is represented through a family of probability distributions. Then information geometry is an appropriate context in which to provide for such analytic work, whether with maximum likelihood fitted distributions or empirical frequency distributions. The important provision is of a natural geometric measure structure on families of probability distributions by representing them as Riemannian manifolds. Then the distributions are points lying in this geometrical manifold, different features can be identified and dissimilarities computed, so that neighbourhoods of objects nearby a given example object can be constructed. This can reveal clustering and projections onto smaller eigen-subspaces which can make comparisons easier to interpret. Geodesic distances can be used as a natural dissimilarity metric applied over data described by probability distributions. Exploring this property, we propose a new face recognition method which scores dissimilarities between face images by multiplying geodesic distance approximations between $3$-variate RGB Gaussians representative of colour face images, and also obtaining joint probabilities. The experimental results show that this new method is more successful in recognition rates than published comparative state-of-the-art methods.

Item Type:	Article
Uncontrolled Keywords:	entropy; information geometry; cyber security; classification; feature recognition; retrieval
Subjects:	MSC 2010, the AMS's Mathematics Subject Classification > 53 Differential geometry MSC 2010, the AMS's Mathematics Subject Classification > 60 Probability theory and stochastic processes MSC 2010, the AMS's Mathematics Subject Classification > 62 Statistics
Depositing User:	Prof CTJ Dodson
Date Deposited:	11 Jul 2021 08:44
Last Modified:	11 Jul 2021 08:44
URI:	https://eprints.maths.manchester.ac.uk/id/eprint/2827

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