Review reveals optimization strategies in facial recognition systems, indicating that genetic algorithms effectively address processing delays and image sensitivity.
The paper examines the main drawbacks of modern face recognition algorithms: low processing speed, high sensitivity to image quality and face positioning. A division into three approaches to face recognition algorithms optimization is proposed: optimization of feature weights, algorithm hyperparameters, and constructing an optimal distributed system architecture. Examples of the application of Particle Swarm Optimization, Cuckoo Search, Simulated Annealing, and genetic algorithms to overcome the mentioned limitations in existing algorithms are provided. The study demonstrates the advantages and disadvantages of these optimization methods and identifies promising directions for further research in face identification methods optimization using genetic algorithms.
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Sitkov et al. (2025) studied this question.
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