Open Access

ARTICLE

Voice to Face Recognition Using Spectral ERB-DMLP Algorithms

Fauzi A. Bala^1,2,*, Osman N. Ucan¹, Oguz Bayat¹

1 School of Engineering and Natural Science, Altinbas University, 34217, Turkey
2 College of Engineering, Electrical and Electronic Engineering, Misurata University, Libya

* Corresponding Author: Fauzi A. Bala. Email:

Computers, Materials & Continua 2022, 73(1), 2187-2204. https://doi.org/10.32604/cmc.2022.024205

Received 08 October 2021; Accepted 18 March 2022; Issue published 18 May 2022

Abstract

Designing an authentication system for securing the power plants are important to allow only specific staffs of the power plant to access the certain blocks so that they can be restricted from using high risk-oriented equipment. This authentication is also vital to prevent any security threats or risks like compromises of business server, release of confidential data etc. Though conventional works attempted to accomplish better authentication, they lacked with respect to accuracy. Hence, the study aims to enhance the recognition rate by introducing a voice recognition system as a personal authentication based on Deep Learning (DL) due to its ability to perform effective learning. The study proposes Equivalent Rectangular Bandwidth and Deep Multi-Layer Perceptron (ERB-DMLP) as it has the ability to perform efficient and relevant feature extraction and faster classification. This algorithm also has the ability to establish effective correlation between voices and images and achieve the semantic relationship between them. Voice pre-processing is initially performed to make it suitable for further processing by removing the noise and enhancing the quality of signal. This process is also vital to minimize the extra computations so that the overall efficacy of the system can be made flexible by considering the audio files as features and the images as labels to identify a person’s voice by classifying the extracted features from the ERB Feature Extraction. This is then passed as the input into DMLP model to classify the persons, and trained the model to make an accurate classification of audio with corresponding image labels, and perform the performance test based on the trained model. Flexibility, relevant feature extraction and faster classification ability of the proposed work has made it explore better outcomes that is confirmed through results.

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Keywords

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