New Technique for Face Recognition using Optimization Algorithms with PCA and Euclidean Classifier
DOI:
https://doi.org/10.65204/djes.v3i3.653Keywords:
Particle Swarm Optimization Octopus Optimization Face Recognition Euclidean distance classifier Principal Component AnalysisAbstract
In face recognition technology, feature selection is an optimization method. By eliminating unnecessary, noisy, and redundant data, feature selection improves the database's ability to recognize faces. This research introduces an innovative method that combines Particle Swarm Optimization and Octopus Optimization Algorithm meta-heuristics to improve the selection of facial recognition features in the ORL database. The system initially employs Principal Component Analysis for feature extraction. Tackling the problem of weak discriminating ability in unprocessed Principal Component Analysis features, the hybrid system serves as a complex-oriented determinant. The method utilizes Particle Swarm Optimization for strong local exploitation and the Octopus Optimization Algorithm for regular global exploration, ensuring an algorithm that escapes local optima to determine the best feature subset. Employing the Euclidean distance classifier enhances recognition precision through optimization. Performance evaluation using Cumulative Match Curve, Receiver Operating Characteristic, and Expected Performance curves confirms that the hybrid system successfully filters Principal Component Analysis components, leading to markedly improved rank-1 recognition rates. This supports the hybrid method for achieving high-precision, resilient face recognition, with accuracy ranging from 98% to 100%.
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