Photonic human identification based on deep learning of back scattered laser speckle patterns

Zeev Kalyzhner, Or Levitas, Felix Kalichman, Ron Jacobson, Zeev Zalevsky

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

The analysis of the dynamics of speckle patterns that are generated when laser light is back scattered from a tissue has been recently shown as very applicable for remote sensing of various bio-medical parameters. In this work, we present how the analysis of a static single speckle pattern scattered from the forehead of a subject, together with advanced machine learning techniques based on multilayered neural networks, can offer novel approach to accurate identification within a small predefined number of classes (e.g., a ‘smart home’ setting which restricts its operations for family members only). Processing the static scattering speckle pattern by neural networks enables extraction of unique features with no previous expert knowledge being required. Using the right model allows for a very accurate differentiation between desirable categories, and that model can form a basis for using speckles patterns as a form of identity measure of ‘forehead-print’.

Original languageEnglish
Pages (from-to)36002-36010
Number of pages9
JournalOptics Express
Volume27
Issue number24
DOIs
StatePublished - 25 Nov 2019

Bibliographical note

Publisher Copyright:
© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement.

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