A Robust Authentication and Authorization System Powered by Deep Learning and Incorporating Hand Signals

Palarimath, Suresh; Blessing, N. R. Wilfred; Sujatha, T.; Pyingkodi, M.; Ugalde, Bernard H.; Palarimath, Roopa Devi

doi:10.1007/978-981-16-7610-9_77

Cited by 8 publications

(2 citation statements)

References 14 publications

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“…Another study with high accuracy rates was carried out to provide convenience for the elderly and disabled people and to authenticate through sign language. In this study, Raspberry Pi 3, which is a more advanced board than used in this project, was used [9].…”

Section: Related Workmentioning

confidence: 99%

Authentication with face recognition and sign language using ESP32-CAM

YALÇIN,

TÜRKDAĞLI,

DALKILIÇ

et al. 2023

DEUFMD

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Authentication is the confirmation of the accuracy of the data piece that any institution, person or system accepts as correct. Many methods are used for the authentication process. Some of these are the methods using biometric data such as face authentication, fingerprint authentication, and iris authentication. In this article, the way to create a secure system using face recognition and sign language as an authentication method is discussed and an application using ESP32-CAM is developed and tested. The results show that secure authentication cannot be achieved with facial recognition and sign language. The developed system is low cost and easy to implement. With this system, authentication can be done without requiring any physical contact, and it can be used for personal security and entrance and exit. Sign language, which is frequently used by hearing-impaired individuals, can play an active role as authentication. With low-cost modules such as ESP32, it will be an alternative to authentication in almost every environment.

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Section: Related Workmentioning

confidence: 99%

Authentication with face recognition and sign language using ESP32-CAM

YALÇIN,

TÜRKDAĞLI,

DALKILIÇ

et al. 2023

DEUFMD

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“…Edge computing, among other things, aids the operation of smart cities. The edge's computing capabilities improve security and confidentiality by processing critical data at the border and delivering processed, protected, or symmetric encryption through the cloud 4 . The Internet of Things allows enterprises to gain real‐time insight into everything from routine business operations to machinery, logistics, and supply chains.…”

Section: Introductionmentioning

confidence: 99%

Secure personal authentication in fog devices via multimodal rank‐level fusion

Selvaraj

Thangavel

2023

Concurrency and Computation

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Summary In recent years, Fog and edge computing have gained enormous increases and usage of Internet of Things (IoT) devices in research and development even though it provides challenges such as security and privacy. One way to ensure this is a fusion of multimodal biometrics authentication systems, which offer highly reliable biometric authentication solutions to reduce the failure to enroll rate, increase the degree of freedom, and deter spoof attacks. Since unimodal biometric systems often face significant limitations due to sensitivity to noise, data quality, non‐universality, and illumination variations. In this research, we propose management of access control to ensure the desired level of security, a Rank level fusion integration method. ORL, GT, CASIA, and VGG‐face databases were used for testing and evaluating the implemented system. Thus, using the LDA with SCE based feature selection approach, the average GAR percent for the facial, hearing, and palm vein is 3.25% for the facial and muffs pulse and 8.55% for the head and palm vein result was the overall performance of the biometric system even in the presence of low‐quality data in the Fog environment.

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