Abstract
Every place be it a household or organization, big or small, like banks have something that needs to be secured to ensure efficient operations and management. Security is always a concern as what is being protected is valuable. Security systems based on singular or multiple biometrics such as face, voice, iris, fingerprint and palm along with things being carried in person such as RFID card or security key(s) are used along with or instead of pin, password based existing lock systems is mostly used because of the uniqueness and added layer of security provided by the aforementioned features. But the implementation of these features alone is not sufficient to thwart any malicious actions to gain access to a secure location, due to rise in technology capable of beating/bypassing said security systems. Thus, this paper proposes a robust security system that will be take care of security requirements of any location that might contain something valuable and to be retrofitted with the problems prevailing in the present systems. The proposed system is capable of detecting & recognizing a person’s face, their emotion based on facial expression, the liveliness factor of their face to determine physical presence, identifying the speaker along with a word/phrase in their speech and detecting factors in the surrounding environment that may threaten a user. The system is designed in a way such that anyone who wants to enter/access a secure location has to pass through all of these layers like password, facial recognition, facial emotion recognition, facial liveliness recognition, speaker recognition, speaker phrase detection, and environmental threat detection etc. of security working in unison, none of which can be bypassed easily. All the sensors for detecting, identifying and recognizing said biometric features are securely connected to a singular security device to ensure success of this goal.
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Krishna Khanth, N., Jain, S., Madan, S. (2023). Sentinel: An Enhanced Multimodal Biometric Access Control System. In: Sachdeva, S., Watanobe, Y., Bhalla, S. (eds) Big Data Analytics in Astronomy, Science, and Engineering. BDA 2022. Lecture Notes in Computer Science, vol 13830. Springer, Cham. https://doi.org/10.1007/978-3-031-28350-5_8
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