JOLO GUARD: SMART DOOR ACCESS WITH VOICE ASSISTANT
Keywords:
NilAbstract
In the era of smart homes and Internet of Things (IoT), there is a growing need for advanced security systems that are not only reliable but also user-friendly. This research paper introduces JOLO GUARD, a voice recognition-based access control system designed to provide secure and convenient entry into rooms. His research paper presents "JOLO GUARD," a voice recognition project aimed at revolutionizing the way individuals gain entry into restricted spaces. Leveraging advanced machine learning algorithms, JOLO GUARD offers a seamless and efficient method of authentication based on the unique characteristics of an individual's voice. By capturing and analyzing vocal patterns in real-time, the system compares the input voice sample with pre-registered profiles stored in a secure database. Access is granted only when a positive match is detected, ensuring that authorized individuals can enter the room with ease. Furthermore, JOLO GUARD incorporates robust security measures to deny entry to unauthorized individuals, thus bolstering the overall security of the premises. Through its intuitive interface and reliable performance, JOLO GUARD provides a user-friendly solution for access control across various environments, including residential, commercial, and institutional settings. This paper details the development and implementation of JOLO GUARD, presenting experimental results that showcase its efficacy, accuracy, and potential applications. By offering a novel approach to access control through voice recognition technology, JOLO GUARD represents a significant advancement in the field of security systems, paving the way for safer and more convenient access management solutions in the future.
References
Poushneh, Atieh. "Humanizing voice assistant: The impact of voice assistant personality on consumers’ attitudes and behaviors." Journal of Retailing and Consumer Services 58 (2021): 102283.
https://www.sciencedirect.com/science/article/pii/S0969698920312911
Nasirian, Farzaneh, Mohsen Ahmadian, and One-Ki Daniel Lee. "AI-based voice assistant systems: Evaluating from the interaction and trust perspectives." (2017).
Yan, Chen, et al. "A survey on voice assistant security: Attacks and countermeasures." ACM Computing Surveys 55.4 (2022): 1-36. https://dl.acm.org/doi/abs/10.1145/3527153
Subhash, S., et al. "Artificial intelligence-based voice assistant." 2020 Fourth world conference on smart trends in systems, security and sustainability (WorldS4).
IEEE, 2020. https://ieeexplore.ieee.org/abstract/document/9210344/
Hoy, Matthew B. "Alexa, Siri, Cortana, and more: an introduction to voice assistants." Medical reference services quarterly 37.1 (2018): 81-88.
https://www.tandfonline.com/doi/abs/10.1080/02763869.2018.1404391
Guha, Abhijit, et al. "How artificiality and intelligence affect voice assistant evaluations." Journal of the Academy of Marketing Science 51.4 (2023): 843-866.
https://link.springer.com/article/10.1007/s11747-022-00874-7
Polyakov, E. V., et al. "Investigation and development of the intelligent voice assistant for the Internet of Things using machine learning." 2018 Moscow Workshop on Electronic and Networking Technologies (MWENT). IEEE, 2018. https://ieeexplore.ieee.org/abstract/document/8337236/
Cheng, Peng, and Utz Roedig. "Personal voice assistant security and privacy—a survey." Proceedings of the IEEE 110.4 (2022): 476-507.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Journal of Applied Optics
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The CC Attribution-NonCommercial 4.0 License allows sharing and adapting the work, provided the creator is credited and the work is not used commercially. Modifications must be indicated, and derivative works under the same license are allowed.