Biometric Authentication using Gait Recognition

Authors

  • Gupta M

DOI:

https://doi.org/10.36676/urr.2023-v10i4-001

Abstract

The necessity for trustworthy and secure means of user identification has become critical in a society that is becoming more and more digital. The widespread use of biometric authentication has emerged as a possible answer to the hacker-proneness of conventional authentication techniques like passwords and PINs. Biometric authentication uses a person's distinctive physiological and behavioral traits to confirm their identity. "Biometric Authentication using Gait Recognition" is one such new and creative strategy. The technique of identifying people based on their physiological or behavioral attributes, such as fingerprints, facial features, speech patterns, and gait, is known as biometric authentication. A subfield of biometrics called "gait recognition" aims to identify people by observing how they move or walk. Every person has a unique gait pattern, which is impacted by things including body composition, weight distribution, and muscle activity. Gait recognition technology is an appealing alternative to conventional approaches because it can record and analyze these minute differences to produce a biometric profile.

References

Álvarez-Aparicio C, Guerrero-Higueras ÁM, González-Santamarta MÁ, Campazas-Vega A, Matellán V, Fernández-Llamas C. Biometric recognition through gait analysis. Sci Rep. 2022 Aug 25;12(1):14530. doi: 10.1038/s41598-022-18806-4. PMID: 36008528; PMCID: PMC9406276. Das S, Meher S, Sahoo UK. A Unified Local-Global Feature Extraction Network for Human Gait Recognition Using Smartphone Sensors. Sensors (Basel). 2022 May 24;22(11):3968. doi: 10.3390/s22113968. PMID: 35684589; PMCID: PMC9182843.

Hoang T, Choi D. Secure and privacy enhanced gait authentication on smart phone. ScientificWorldJournal. 2014;2014:438254. doi: 10.1155/2014/438254. Epub 2014 May 14. PMID: 24955403; PMCID: PMC4052054.

Salvador-Ortega I, Vivaracho-Pascual C, Simon-Hurtado A. A New Post-Processing Proposal for Improving Biometric Gait Recognition Using Wearable Devices. Sensors (Basel). 2023 Jan 17;23(3):1054. doi: 10.3390/s23031054. PMID: 36772096; PMCID: PMC9919966.

Sprager S, Juric MB. Inertial Sensor-Based Gait Recognition: A Review. Sensors (Basel). 2015 Sep 2;15(9):22089-127. doi: 10.3390/s150922089. PMID: 26340634; PMCID: PMC4610468.

Tian Y, Wei L, Lu S, Huang T. Free-view gait recognition. PLoS One. 2019 Apr 16;14(4):e0214389. doi: 10.1371/journal.pone.0214389. PMID: 30990804; PMCID: PMC6467377.

Wang X, Yan WQ. Human Gait Recognition Based on Frame-by-Frame Gait Energy Images and Convolutional Long Short-Term Memory. Int J Neural Syst. 2020 Jan;30(1):1950027. doi: 10.1142/S0129065719500278. Epub 2019 Sep 18. PMID: 31747820. Zeng X, Zhang X, Yang S, Shi Z, Chi C. Gait-Based Implicit Authentication Using Edge Computing and Deep Learning for Mobile Devices. Sensors (Basel). 2021 Jul 5;21(13):4592. doi: 10.3390/s21134592. PMID: 34283149; PMCID: PMC8271781.

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Published

2023-11-27
CITATION
DOI: 10.36676/urr.2023-v10i4-001
Published: 2023-11-27

How to Cite

Gupta, M. (2023). Biometric Authentication using Gait Recognition. Universal Research Reports, 10(4), 1–9. https://doi.org/10.36676/urr.2023-v10i4-001

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Original Research Article