Department of Computer Engineering

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    Influence of Eigenvector on Selected Facial Biometric Identification Strategies
    (World Journal of Engineering Research and Technology, 2020-02-16) Jooda, Janet
    Face identification strategies are becoming more popular among biometric-based strategies as it measures an individual‟s natural data to authenticate and identify individuals by analyzing their physical characteristics. For face identification system to be efficient and robust to serve it purpose of security, there is need to use the best strategy out of the many strategies that have been proposed in literatures for face identification. Amidst the most popularly used face identification strategies, Principal Component Analysis PCA, Binary Principal Component Analysis BPCA, and Principal Component Analysis – Artificial Neural Network PCA-ANN were selected for performance evaluation. The research was experimented by varying the eigenvector of the training images for each strategy to compare the performance using Recognition Rate RR and Total Recognition Time TR as performance metrics. Results showed that PCA – ANN strategy gave the best recognition rate of 94% with a trade-off in recognition time. Also, the recognition rates of PCA and B-PCA increased with decreasing number of eigenvectors but PCA-ANN recognition rate was negligible. Hence PCA-ANN outperforms the other face identification strategies.
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    A Review on Hybrid Artificial Bee Colony for Feature Selection
    (Global Journal of Advanced Research, 2021-08-30) Jooda, Janet
    Due to the presence of redundant and irrelevant features in the dataset, the feature space's high dimensionality has an impact on classification accuracies and computational complexity. Feature Selection gets the most relevant and valuable information and aids in classification speed. Since finding the suitable, optimal feature subset is critical, feature selection is viewed as an optimization problem. One of the efficient nature-inspired optimization techniques for handling combinatorial optimization issues is the Artificial Bee Colony algorithm. It has no sensitive control parameters and has been demonstrated to compete with other well-known algorithms. However, it has a poor local search performance, with the equation of solution search in ABC performing well for exploration but poorly for exploitation. Furthermore, it has a quick convergence rate and can thus become caught in local optima for some complex multimodal situations. Since its introduction, much research has been conducted to address these issues in order to make ABC more efficient and applicable to a wide range of applications. This paper provides an overview of ABC advances, applications, comparative performance, and future research opportunities.
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    Fingerprint Intramodal Biometric System Based on ABC Feature Fusion
    (Asian Journal of Research in Computer Science, 2021-08-13) Jooda, Janet
    Unimodal biometrics system (UBS) drawbacks include noisy data, intra-class variance, inter-class similarities, non-universality, which all affect the system's classification performance. Intramodal fingerprint fusion can overcome the limitations imposed by UBS when features are fused at the feature level as it is a good approach to boost the performance of the biometric system. However, feature level fusion leads to high dimensionality of feature space which can be overcame by Feature Selection (FS). FS improves the performance of classification by selecting only relevant and useful information from extracted feature sets being an optimization problem. Artificial Bee Colony (ABC) is an optimizing algorithm that has been frequently used in solving FS problems because of its simple concept, use of few control parameters, easy implementation and good exploration characteristics. ABC was proposed for optimized feature selection prior to the classification of Fingerprint Intramodal Biometric System (FIBS). Performance evaluation of ABCbased FIBS showed the system had a Sensitivity of 97.69% and RA of 96.76%. The developed ABC optimized feature selection reduced the high dimensionality of features space prior to classification tasks thereby increasing sensitivity and recognition accuracy of FIBS.
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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.