Department of Computer Engineering

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Browsing Department of Computer Engineering by Author "Jooda, Janet"

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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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    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.