Abstract
Abstract This study examines the effectiveness of Genetic Neural Networks (GNN) in face recognition, particularly in optimizing parallel algorithms to overcome the challenges posed by complex data. We have significantly improved recognition accuracy and computational efficiency by employing an adaptive genetic algorithm that fine-tunes neural network weights through Selection, crossover, and mutation. Our approach was tested across diverse datasets, covering variations in posture, age, ethnicity, and lighting conditions. The results demonstrate outstanding recognition rates: 99.82% on LFW, 97.94% on AgeDB-30, 95.11% on CFP-FP, 95.87% on CALFW, and 89.44% on CPLFW, showcasing exceptional robustness against complex lighting and occlusions. Additionally, our algorithm maintains balanced accuracy across different ethnicities with an overall recognition rate of 96.77% and boasts a substantial reduction in processing time to an average of 4.15 seconds. These advancements underscore the potential and practicality of our method in enhancing face recognition technology.
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