Some Information Geometric Aspects of Cyber Security by Face Recognition
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/23/7/878 |
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| author | C. T. J. Dodson John Soldera Jacob Scharcanski |
| author_facet | C. T. J. Dodson John Soldera Jacob Scharcanski |
| author_sort | C. T. J. Dodson |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T09:40:29Z |
| format | Article |
| id | doaj.art-b8ec4a6aa3b4474a8c9739ca15a77392 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T09:40:29Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-b8ec4a6aa3b4474a8c9739ca15a773922023-11-22T03:45:12ZengMDPI AGEntropy1099-43002021-07-0123787810.3390/e23070878Some Information Geometric Aspects of Cyber Security by Face RecognitionC. T. J. Dodson0John Soldera1Jacob Scharcanski2School of Mathematics, University of Manchester, Manchester M13 9PL, UKFederal Institute of Education, Science and Technology Farroupilha, Santo Ângelo 98806-700, BrazilInstitute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre 91501-970, BrazilSecure 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.https://www.mdpi.com/1099-4300/23/7/878entropyinformation geometrycyber securityclassificationfeature recognitionretrieval |
| spellingShingle | C. T. J. Dodson John Soldera Jacob Scharcanski Some Information Geometric Aspects of Cyber Security by Face Recognition Entropy entropy information geometry cyber security classification feature recognition retrieval |
| title | Some Information Geometric Aspects of Cyber Security by Face Recognition |
| title_full | Some Information Geometric Aspects of Cyber Security by Face Recognition |
| title_fullStr | Some Information Geometric Aspects of Cyber Security by Face Recognition |
| title_full_unstemmed | Some Information Geometric Aspects of Cyber Security by Face Recognition |
| title_short | Some Information Geometric Aspects of Cyber Security by Face Recognition |
| title_sort | some information geometric aspects of cyber security by face recognition |
| topic | entropy information geometry cyber security classification feature recognition retrieval |
| url | https://www.mdpi.com/1099-4300/23/7/878 |
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