Secure authentication using a multidimensional retinal biometric encryption method

Biometric encryption integrates physiological traits with cryptographic operations to improve authentication security. Retinal vasculature is particularly attractive due to its internal protection, permanence, and high inter-subject variability. we present a revised and rigorously justified multidimensional retinal encryption framework that generates three independent keys—RDDM (Retinal Diagonal Distance Metric), ROTD (Radial Origin-Terminus Distance), and DRID (Diagonal–Radial Intersection Distance)—from a single retinal vessel map. This framework is intended as a biometric-driven key generation and strengthening module to enhance user authentication, rather than a standalone standard encryption algorithm. It operates under a threat model focused on resisting brute-force key guessing in controlled biometric contexts, but not advanced attacks like quantum cryptanalysis or side-channel exploitation. Retinal images undergo preprocessing (CLAHE, vessel segmentation, skeletonization, endpoint detection) to extract stable endpoints. These endpoints produce distance measures that are normalized and combined into polyalphabetic key streams. We provide stepwise derivations of the encryption E(x) and decryption D(y) equations, explicitly justify mod 124 as the symbol table size used in implementation, and include a detailed cryptanalytic evaluation (entropy, NIST SP800-22 randomness tests, Hamming distance, collision analysis, noise sensitivity, and Full-Space Key Guessing (FSKG) calculations). Experimental results on three retinal samples (n_vessels = 27,41,105) show substantially increased FSKG times and near-maximal key entropy relative to single-key baselines. Limitations and sensitivity to image quality are discussed.

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Publication Details

Journal
Scientific Reports
Published
2026-03-16
DOI
https://doi.org/10.1038/s41598-026-40962-0
Primary Topic
Biometric Identification and Security
Type
article
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article

Secure authentication using a multidimensional retinal biometric encryption method

Yashmin Banu, Biplab Kumar Rath, Debasis Gountia
Scientific Reports
Biometric Identification and Security
article

Secure authentication using a multidimensional retinal biometric encryption method

Yashmin Banu, Biplab Kumar Rath, Debasis Gountia
article en

Abstract

Biometric encryption integrates physiological traits with cryptographic operations to improve authentication security. Retinal vasculature is particularly attractive due to its internal protection, permanence, and high inter-subject variability. we present a revised and rigorously justified multidimensional retinal encryption framework that generates three independent keys—RDDM (Retinal Diagonal Distance Metric), ROTD (Radial Origin-Terminus Distance), and DRID (Diagonal–Radial Intersection Distance)—from a single retinal vessel map. This framework is intended as a biometric-driven key generation and strengthening module to enhance user authentication, rather than a standalone standard encryption algorithm. It operates under a threat model focused on resisting brute-force key guessing in controlled biometric contexts, but not advanced attacks like quantum cryptanalysis or side-channel exploitation. Retinal images undergo preprocessing (CLAHE, vessel segmentation, skeletonization, endpoint detection) to extract stable endpoints. These endpoints produce distance measures that are normalized and combined into polyalphabetic key streams. We provide stepwise derivations of the encryption E(x) and decryption D(y) equations, explicitly justify mod 124 as the symbol table size used in implementation, and include a detailed cryptanalytic evaluation (entropy, NIST SP800-22 randomness tests, Hamming distance, collision analysis, noise sensitivity, and Full-Space Key Guessing (FSKG) calculations). Experimental results on three retinal samples (n_vessels = 27,41,105) show substantially increased FSKG times and near-maximal key entropy relative to single-key baselines. Limitations and sensitivity to image quality are discussed.

Scientific ReportsVol. 16(1)
Institute of Physics, Bhubaneshwar (IN), GIET University (IN), Indian Institute of Technology Bhubaneswar (IN)
Openalex Percentile: Top 21%
Biometric Identification and Security
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