Browsing by Author "Ajayi, Adebimpe Ruth"

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    Enhancing Attendance Management with Facial Recognition: A Web and Mobile-Based System
    (2025-03) Ajayi, Adebimpe Ruth; Omotola, David A.; Olanrewaju Mubarak D.; Ajayi, Mark O.; Adebayo, Olalekan F.; Adesina Ayodele J.
    The development of a facial recognition-based attendance management system is presented in this study. By leveraging facial recognition technology, the system offers a reliable, efficient, and secure alternative to traditional methods such as manual roll calls and paper-based records, which are prone to errors and manipulation. The system employed an Android application to capture students' facial images, which are then processed using advanced Image Processing APIs, including OpenCV and the Python Face Recognition library, to identify and authenticate individuals. The Dlib open-source library which uses a Histogram of Oriented Gradients (HOG) and linear Support Vector Machine (SVM) was used as the face detection model. The system’s performance was evaluated using the False Acceptance Rate (FAR) and False Rejection Rate (FRR) metrics. The results indicated a FAR of 0%, ensuring the system effectively blocks illegitimate attendance entries. However, an FRR of approximately 5% was observed, highlighting challenges in accurately identifying legitimate users under varying conditions such as changes in lighting and facial expressions. Keywords— Attendance Management, False Acceptance Rate (FAR), False Rejection Rate (FRR), OpenCV
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    Experimental Assessment of Spectral Subtraction and Kalman Filtering Algorithm on Electricity Generator Noise Reduction in Wireless Communication System
    (2024) Olawole, Esther Toyin; Akande, Damilare Oluwole; Adeyemo, Zaccheaus K.; Ajayi, Adebimpe Ruth; Ariba, Folashade O.; Adebayo, Olalekan F.
    Noise corrupts, slows down, and reduces the clarity or accuracy of communication. It prevents an undistorted signal or message from being transmitted over wireless communication systems. To reduce noise in wireless communication channels, methods like spectral subtraction and Kalman filter can be used. Spectral subtraction uses its subtractive ability to remove noise in a noisy speech signal, while Kalman filter provides estimates of some unknown noise variables given the measurements observed over time. Therefore, this paper proposes an experimental assessment of both Spectral subtraction (SS) and Kalman Filtering (KF) algorithm for an electricity generator noise-corrupted speech signal over a wireless communication system at the receiver’s end. The proposed assessment was carried out using noisy speech signals obtained from a conventional mobile phone and the evaluation was carried out using generator noisy speech signals recorded in a workshop where a generator is been used while, the experimental assessment was performed using MATLAB software. The noise attenuation techniques evaluation was analysed using Mean Square Error (MSE), Signal-to-Noise Ratio (SNR), Perceptual Evaluation of Speech Quality (PESQ), and Short-Time Objective Intelligibility (STOI). The analysis revealed that Kalman filter performed better than spectral subtraction in reducing generator noise in wireless communication systems. Keywords: Spectral subtraction, Kalman filter, signal-to-noise ratio, perceptual evaluation of speech quality, short-time objective intelligibility.
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    Liquefied Petroleum Gas Concentration Monitoring System with Alarm and Cloud- Based Logging
    (Faculty of Engineering, University of Benin, Benin City, Edo State, Nigeria., 2021) Ajiboye, Aye Taiwo; Opadiji, Jaiyeola Femi; Popoola, Joshua Oluwshogo; Ajayi, Adebimpe Ruth