DOI : https://doi.org/10.18517/ijaseit.12.6.16049

Access Control System based on Voice and Facial Recognition Using Artificial Intelligence

José Capote-Leiva (1), Marco Villota-Rivillas (2), Julián Muñoz-Ordóñez (3)
(1) Department of Engineering, Corporación Universitaria Comfacauca, Unicomfacauca, Popayán, 190003, Colombia
(2) Department of Engineering, Corporación Universitaria Comfacauca, Unicomfacauca, Popayán, 190003, Colombia
(3) Department of Engineering, Corporación Universitaria Comfacauca, Unicomfacauca, Popayán, 190003, Colombia
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How to cite (IJASEIT) :
Capote-Leiva, José, et al. “Access Control System Based on Voice and Facial Recognition Using Artificial Intelligence”. International Journal on Advanced Science, Engineering and Information Technology, vol. 12, no. 6, Dec. 2022, pp. 2342-8, doi:10.18517/ijaseit.12.6.16049.
Citation Format :
Computer security has become a matter of great concern at the global level. Whatever the economic sector, all companies handle confidential information related to clients and personnel. These latter therefore need to be seen as sensitive assets that require protection. Appropriate, consensual handling of personal information is thus a legal and delicate requirement that demands to be treated securely in all types of business. Many companies and clients have lost sums of money in the millions due to incorrect information protection, triggering complicated, expensive proceedings that are awkward and cumbersome to resolve. To implement an authentication system based on biometric parameters, which strengthens the security of sensitive assets in areas considered critical in various organizations by attaining the highest accuracy in user classification processes. By applying a convolutional neural network: MobileNet, viewing via computer and low-cost devices (Raspberry Pi 3). The system constitutes an authentication device for voice and face with 96% and 100% accuracy, respectively. Conclusion: The system shows that deep learning (Deep Convolutional Neural Networks), in combination with devices such as the Raspberry, generate a system capable of high performance in time and cost, as well as providing companies with a robust system featuring high accuracy in the correct recognition of the biometric patterns of users registered and trained by the system.

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