Department of Computer Engineering

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Browsing Department of Computer Engineering by Author "Ibikunle, Akinola"

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    Development of a face recognition system using hybrid Genetic-principal component analysis
    (1st International Conference on Electrical, Electronic, Computer Engineering & Allied Multidisciplinary Field, 2021-12) Ibikunle, Akinola
    Humans have been using physical attributes such as face, voice gait and fingerprints to recognize each other for ages. With the recent technological advancement, face recognition is a branch of biometrics system which has received considerable interest because of its ease in collecting, analysing and recognising face images. It is a system which compares an unknown image against the trained images in a database in order to identify the image. It has a number of applications such as Automatic Teller Machine (ATM), credit card, physical access control, National Identity card and correctional facilities. It has been found to be one of the ways of controlling and reducing crime rate. The development and evaluation of the performance of a face recognition system using hybrid Genetic- principal component Analysis technique is presented. The system consists of three major subsystems. Initial preprocessing procedures are applied on the input face images selected from the ORL Database. Consequently, face features are extracted from the processed images by principal component analysis and finally face identification is carried out using Genetic algorithm. Image resolutions of 50 x 50, 70 x 70, 100 x 100 and 140 x 140 are used in training and testing the system. The identification rates obtained were 100%, 96.36%, 93.63% and 90.90% for 50 x 50, 70 x 70, 100 x 100 and 140 x 140 respectively. This experimental result revealed that the lower the resolution of the cropped images, the higher the number of the correctly identified face images. The reason is attributed to the fact that there is variation in the features considered for recognition for each resolution. Hence, this technique has been proved to be more robust and suitable for low resolution.
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    Feature Fusion Using GSA for Multi Instance Authentication System
    (Asian Research Journal of Current Science, 2023-11-15) Ibikunle, Akinola
    Multi-instance fusion of fingerprint authentication system at score level overcomes a few of the shortcomings of a Unimodal Biometric System (UBS) and enhanced the efficiency of the system. However, due to loss of information at higher levels, the features fused at the score level are confined in comparison to feature level fusion and could lead to poor performance. In this study, multi-instance fusion of fingerprints was done at feature level using Gravitational Search Algorithm (GSA) to select and combine minimal relevant informative texture features subsets from multi-instances of fingerprint and considerably improves the performance of the system. The approach was validated by creation of multi-instances of fingerprint database acquired locally from 150 subjects in an uncontrolled environment and texture based feature extraction was considered and classification of fused texture feature was done using back propagation neural network. The results show that the presented technique was effective in subject authentication with accuracy of 97.09%, indicating that it can successfully secure fingerprint authentication systems from unauthorized attacks.