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Neural network biometrics – to-self-correcting code converter without redundancy


A.V. Bezyaev

Classical error detecting and correcting codes have significant redundancy, which makes them ineffective in biometric applications. With redundancy more than 50% the classical codes use their primary resources for correcting errors in redundant parts. The author suggests detecting error syndromes by hashing informational pieces of a code that allows one to abandon self-correcting codes redundancy at all in biometric applications. The article describes code recovery procedures and evaluates self-correction time expenditures
June 24, 2020
May 29, 2020

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